Find your research project.

Student parents represent 20% of students in higher education, but many colleges and universities, including Kennesaw State University, are unable to properly identify them. Without standard processes for identification, institutions have a harder time understanding student parent experiences, connecting those students to needed resources, evaluating outcomes, and creating policies that can better support educational goals. This project will task undergraduate researchers with examining how colleges and universities identify and subsequently support student parents and caregivers. Students will complete the new SPARK Collaborative "Family Friendly College Toolkit: Using Data to Improve Outcomes for Caregiving and Caregiving Students”. In addition, they will compare institutional policies and identification practices across colleges within KSU as well as nationwide campuses, and then collect data from KSU students through surveys, interviews, and focus groups if possible. IRB-FY22-74  The First-Year Scholar will develop an evidence-based Student Parent Identification Framework that outlines a process for enrollment initiatives to identify student parents to improve access to campus resources. They will also explore how these resources have assisted students so far as well as how better identification can strengthen student success efforts.  This project addresses a clear gap in how institutions identify and support student parents by integrating higher education research with policy analysis. Findings will be shared through presentations, posters, potential policy briefs, and recommendations that may inform decision-making at KSU and can also serve as a potential pilot model for other USG institutions. Students interested in pursuing social work, education, political science, public health, and/or psychology should apply.

The primary goal of this project is to use a portable, light-based sensor to investigate age-related differences in muscle blood flow and oxygenation in young and older adults. Aging is often associated with a progressive decline in muscle mass, strength, and function, which can contribute to reduced physical function, fatigue, disability, and an increased risk of falls and injuries. Changes in muscle blood flow and the delivery and use of oxygen may contribute to age-related declines in muscle function. Therefore, measuring these physiological changes during exercise may provide useful information about muscle vascular health. However, many existing methods for measuring muscle blood flow are expensive, operator dependent, limited to laboratory settings, or unable to simultaneously provide information about blood flow and oxygenation. The proposed portable optical sensor uses two wavelengths of near-infrared light and a camera to continuously and noninvasively monitor muscle blood flow and changes in oxygenation during exercise. This project will analyze previously collected optical sensor data from younger and older adults during a handgrip exercise protocol. Changes in muscle blood flow and oxygenation at rest, during exercise, and recovery will be compared between the two age groups. In addition to measuring overall changes in blood flow and oxygenation, this project will also examine the shape of the pulsatile blood flow signal, which may provide additional information about age-related changes in blood vessel function and response.  In this project, the undergraduate student will gain hands-on experience operating the optical sensor and performing tissue-mimicking phantom experiments to evaluate sensor response and performance. The student will also analyze previously collected human data and learn how optical sensors can be used to study muscle physiology. Using programming-based signal-processing algorithms, the student will extract and compare blood flow, oxygenation, and pulsatile waveform features between young and older adults. The student will also gain experience in basic statistical analysis, interpretation of experimental results, and scientific research while working closely with the Translational Biomedical Optics research team. In addition, the student will develop scientific communication skills by preparing research figures, symposium abstract, and presentation. Overall, this project will provide interdisciplinary research experience combining engineering, biomedical optics, physiology, programming, and data analysis, while contributing to a better understanding of age-related changes in muscle blood flow and oxygenation and their potential use in evaluating muscle vascular health.

This project will look at how two different types of mentally tiring activities impact how you perform physically. We will use devices that measure strength, blood flow to the brain, heart rate, and muscle activity during physical activity. The purpose of doing this type of research is to have a better understanding of how mental fatigue can make you perform when tired. Do you ever feel tired after a test or a long day of class, then try to workout or play sports? Did you feel at your best? Even though you did not do anything physically active, your brain tells you that you are tired and need to rest! We are looking into how different types of mentally fatiguing tasks influence how you perform as well as reasons why some people respond differently than others. We plan to use several unique pieces of equipment related to measuring force production, muscle electrical activity, heart rate variability, and how blood flow is impacted to the brain during mentally fatiguing activity. This project is a part of a MS Exercise Science Master’s thesis and will be focused on how college students’ physical performance is affected by mental fatigue. We will have participants visit the Exercise Physiology Laboratory a total of three times to perform various mentally fatiguing tasks and strength testing. Each visit should last around 90 minutes. We will compare these differences as participants complete testing over a span of 1-2 weeks. We are aiming to collect data on 40 participants that are college-aged students. Our goal is to identify if blood flow to the brain is altered during mentally fatiguing tasks and how that fatigue may or may not impact physical performance. These findings can help us understand how people respond to different mentally fatiguing tasks and the possible negative impact on our ability to perform physical activity.

This undergraduate research project will engage students in the development and application of functional metal-organic frameworks/carbon nanomaterials as advanced analytical tools for biosensing, point-of-care sensing, and environmental forensics addressing critical challenges in detecting heavy metals, biomarkers such as dopamine for neurological disorders and hydrogen peroxide associated with breast cancer, glucose for diabetes, and DNA for many diseases in complex environmental and biological matrices. In addition, the project will explore the application of these advanced materials to address emerging challenges in carbon capture from air and solar energy harvesting. Project 1: Fabrication of Functional Nanomaterials for Sensing Biomolecules, Biomarkers, and Environmental Forensics  The research project focuses on the design, synthesis, characterization, and application of functional metal-organic framework/carbon nanomaterials for sensitive and selective detection of ultra-trace amounts of biomarkers and environmental forensic species. Students will investigate how nanoscale composition, structure, morphology, and functionalization influence sensing performance. Research will involve developing nanomaterial-based platforms for detecting clinically relevant biomarkers (dopamine, glucose, hydrogen peroxide, and DNA), pathogens, and environmental forensic contaminants (PFAS, heavy metals, antibiotics, etc.). A particular emphasis will be placed on bridging the gap between conventional clinical diagnostics and point-of-care detection by exploring materials and sensing strategies that can provide rapid, sensitive, selective, and low-cost analysis. Project 2: Design and Synthesis Nanomaterials for Carbon Capture from Air and Conversion to Fuels and Feedstocks This research project will investigate functional metal-organic frameworks (MOFs) nanomaterials capable of capturing carbon dioxide from air and facilitating its electrochemical conversion into fuels and value-added chemicals. Research will examine how MOFs composition, surface properties, catalytic sites, and nanoscale architecture influence atmospheric carbon dioxide adsorption, activation, and conversion. Students will synthesize and characterize novel MOFs nanomaterials and evaluate their performance in carbon capturing from air and transforming it from a waste greenhouse gas into valuable resources. Project 3: Fabrication of Nanostructures for Efficient Solar Energy Harvesting in Solar Cells This research project focuses on designing functional nanomaterials for harvesting and converting solar energy into electricity. Students will investigate dyes and perovskite nanostructured materials with tailored optical, electronic, and electrochemical properties for applications in solar energy harvesting. Depending on the project, research may include nanomaterials for high energy density batteries and supercapacitors applications. This project will examine relationships between nanomaterial structure and energy-conversion or storage performance and will learn to evaluate efficiency, stability, and reproducibility.

Discover how artificial intelligence can bring your ideas to life! In this hands-on research project, first-year KSU scholars will use AI tools to generate creative images, transform those ideas into 3D models, and produce physical objects with 3D printers. Students from all majors are welcome, and no previous experience with AI, 3D modeling, or 3D printing is required—we will teach you everything you need to get started. The project will be completed in person on Kennesaw State University’s Kennesaw Campus and offers an exciting opportunity to explore emerging technology, develop practical skills, and collaborate with other curious and creative students.

This project explores the integration of artificial intelligence (AI) and nanotechnology to advance biomedical diagnostics and therapeutics. Nanoparticles offer unique capabilities for disease detection, imaging, and targeted treatment, while AI provides powerful tools for analyzing complex datasets and accelerating research. The goal of this project is to develop and evaluate nanomedicine technologies using both experimental and computational approaches. Example application areas include targeted drug delivery, cancer thermal therapy, image-guided therapy using multifunctional imaging agents, rapid diagnostic testing, smell training technologies for olfactory rehabilitation, and wearable sensor-based gait analysis and health monitoring. Students will participate in interdisciplinary research focused on the design, characterization, and evaluation of nanoparticle-based systems for biomedical applications through two pathways. Students with interests in computer science, AI, data science, or programming will focus on computational research, including machine learning, computer vision, and data analytics using Python. Students with interests in biology, chemistry, biomedical engineering, or related disciplines will focus on experimental research including nanoparticles synthesis and characterization, development of biosensors, and testing.

Walking robots are typically complex machines that require many motors, sensors, and sophisticated control systems. Our research explores a different question: How simple can a robot be and still walk effectively? During the previous year, our research team developed an initial prototype of the Tripad, a three-legged walking robot designed around the concept of minimal actuation. Unlike conventional walking robots that use multiple motors to control their legs, the Tripad uses one actively driven leg while the other two legs primarily provide support and stability. The initial prototype successfully demonstrated walking, establishing a foundation for the next stage of the research. The original design emphasizes reducing mechanical complexity, power consumption, and cost while maintaining useful locomotion. This year's project will take the Tripad to the next level by incorporating artificial intelligence (AI) and NVIDIA Isaac Sim, a robotics simulation platform. Students will create a virtual version of the existing robot and use simulation to investigate how changes in the robot's structure, actuation, and walking pattern affect its performance. AI and optimization techniques will then be explored to identify designs and movement strategies that could allow the robot to walk more efficiently, smoothly, and reliably while maintaining its simple, minimally actuated design. Students will work as part of a research team to learn robotics, computer-aided design (CAD), simulation, basic programming, 3D printing, and experimental testing. They will compare predictions from the virtual robot with experiments performed on the physical Tripad and use what they learn to modify and improve the prototype. No previous robotics, AI, or programming experience is required. Students need curiosity, creativity, and a willingness to learn. This project provides first-year students with an opportunity to experience the complete engineering research process—from simulation and AI-assisted design to building, testing, and improving a real walking robot.

Modern software systems consist of increasingly large and complex codebases, making it difficult for developers and security analysts to manually understand program behavior and identify potential security vulnerabilities. Traditional program analysis techniques examine source code and program execution to understand how a program is structured, how it behaves, and how information moves through it. However, recent advances in Artificial Intelligence (AI) provide new opportunities to automate this process by learning meaningful representations of code and reasoning about its functionality and potential security weaknesses. In particular, Graph Neural Networks (GNNs) can analyze the structure of a program and how its different components interact, while Large Language Models (LLMs) can analyze source code and reason about its meaning and functionality. These AI approaches provide new opportunities for developing intelligent tools that can automatically understand what software does and identify potentially vulnerable code, a challenge that will be studied in the current First-Year Scholars project. To explore this problem, the students will analyze source code written in commonly used programming languages, such as C, C++, Java or Python. The project will investigate GNN- and LLM-based approaches separately to understand their individual capabilities, as well as evaluate whether combining their structural and semantic information can further improve software analysis. The students will evaluate these approaches on tasks such as identifying the functionality of different software components and detecting known categories of software vulnerabilities.

Georgia is a national leader in poultry production, producing approximately 5.2 billion eggs in 2024. Maintaining egg quality is essential to this economically important industry, but manual inspection and sorting can be labor-intensive, repetitive, and susceptible to inconsistency. In this project, First-Year Scholars (FYS) will investigate how AI, computer vision, and robotics can be integrated to automate egg inspection and sorting. Students will develop a robotic system capable of detecting eggs, identifying visible anomalies, assigning eggs to predefined quality categories, and sorting them accordingly. Students will work with a UFACTORY xArm 6 or xArm Lite robotic arm and an Intel RealSense depth camera. They will gain experience with the system’s hardware and software, program fundamental pick-and-place operations, and integrate visual perception with robotic control. Pretrained AI models will be adapted and evaluated for egg detection, segmentation, and classification. This project will introduce students to research at the intersection of robotics, computer vision, artificial intelligence, and agricultural technology. The resulting prototype could help establish the technical foundation for robotic inspection systems used at farms, grading and distribution facilities, and retail locations. In the longer term, such systems could improve the consistency and efficiency of egg handling while reducing food waste and supporting the delivery of high-quality products to consumers.

The research project is titled “AI/ML-based Low-Power Long-Range Wide-Area Network Management Systems for Internet-of-Energy (IoE) Applications”. This project aims to explore new techniques including (1) AI/ML models for the design of efficient algorithms on energy-starved IoE application networking systems, (2) array antenna control systems to determine beamforming for data transmission reliability and energy efficiency, and (3) a new cloud network paradigm such as a consolidation of Low-Power Wide-Area Network (LPWAN) such as using Long Range Radio (LoRa) technology with edge cloud computing to support QoS both energy-starved IoE and general Internet-of-Thinks (IoT) data communications. LoRa is a low energy consumption, low bit rate, cost-effective, and license-free IoT, which has a significant long range. LoRa can be combined with software-defined networking to create unified, adaptable, rapid-deployable networks. Therefore, these project solutions are applicable to many high-impact situations where communication is lost, as well as monitoring energy intake and usage patterns at the data-driven application site, zone, system, and device level.

This project trains students in environmental data analysis for multidisciplinary applications, no matter what their major. Last year, a team of first-year students on this project won first place in the undergraduate category at the KSU Symposium of Student Scholars, a university-wide event that drew nearly 500 student research projects across all nine colleges. Students will take part in data collection using laser scanners (terrestrial LiDAR) and other advanced tools, capturing rivers, creeks, and landscapes at millimeter-scale resolution. Students will learn to manage, process, and analyze these large datasets on a dedicated high-performance computer, generate 3D models from real-world data, and extract quantitative insights from the datasets. This work addresses the gap in current understanding of freshwater landscape management. No prior experience is required. Students will be trained from the ground up in a toolkit of skills (data collection, data management, spatial analysis, 3D modeling, and scientific communication) that is genuinely interdisciplinary and in high demand across industry, whether students are studying environmental science, geography, engineering, computer science, game design, or another field entirely. Beyond technical training, students will develop both qualitative and quantitative research skills. Their work will contribute to an ongoing longitudinal research study, and students will help prepare a research poster to present at a conference, giving them a concrete, resume-ready research credential and presentation experience before they have even finished their first year. Students will also gain experience collaborating on an interdisciplinary research team and working as both a team member and an independent researcher, skills that carry directly into almost any career path. We are looking for students who are self-motivated, disciplined, and genuinely excited to learn technical skills from scratch, not students looking for a quick resume line. To succeed in this project, students should be able to: • Show up consistently and take ownership of their tasks • Be comfortable being a beginner and pushing through a learning curve • Pay close attention to detail  • Communicate proactively and reliably with their research team • Commit to the project over two semesters, since the strongest outcomes (posters, conference presentations, and co-authorship) come from sustained involvement If students are hardworking, curious, responsible, and want real research experience rather than just a line on a transcript, we want to hear from them.

Connected automated vehicles (CAVs), such as Waymo taxis, have untapped potential to serve as cooperative traffic regulators, working together to reduce congestion in urban road networks. In this research, we are leveraging artificial intelligence (AI) to develop cooperative control strategies that make small adjustments to vehicle speeds and following distances. Although these adjustments may be nearly unnoticeable to passengers, when coordinated across many vehicles, they have the potential to significantly reduce traffic congestion and improve overall traffic flow. To test our reinforcement learning approach, our research team is developing simulation models of 25 major cities across the United States. First-Year Scholars who join the team will work closely with undergraduate researchers to help develop these simulation models, with guidance and support from graduate students. Along the way, students will gain hands-on experience with simulation and artificial intelligence. First-Year Scholars will also have opportunities to learn about the reinforcement learning model. Students who are especially interested in artificial intelligence will be encouraged to contribute to the continued development and testing of our AI control algorithms.

In 1993, the Cobb Country Board of Commissioners voted 3 to 1 to condemn the “gay lifestyle” and withdraw over $40,000 funding from the arts in Cobb Country. This vote was a reaction to Marietta Theatre in the Square's production of Terrance McNally’s play, Lips Together, Teeth Apart. The play features two straight couples who spend the Fourth of July weekend in a gay community on Fire Island and very little risqué material. Nevertheless, convinced of the story’s lurid nature, the Cobb County Board of Commissioners torpedoed the production, hobbled the Theatre in the Square, and set of a firestorm of controversy in the US that ultimately destroyed Cobb County’s chances to host events associated with the 1996 Summer Olympics in Atlanta. Drawing on the Theatre in the Square archives, held here at KSU, this project will produce oral histories and a narrative podcast that tell the story of the controversy surrounding Lips Together, Teeth Apart, explores the censorship of the arts here in Georgia, and forges connections between LGBTQ+ censorship in the 1990s and today. By conducting archival research, oral interviews, and writing and producing the podcast, we'll seek to find out what happens when the arts drive a community apart and how we can come together to heal.

Internal funding or seed funding is often provided by research offices at universities to improve proposals for external funding by helping researchers obtain pilot data to show that their idea is feasible and likely to provide desirable outcomes like peer-reviewed publications. However, this seed funding is often very limited and can be viewed by university leadership as an unnecessary expense. Here at Kennesaw State University, the Research Development unit within the Office of Research oversees a significant amount of internal funding. While increasing external funding to KSU is one of our primary goals, we also aim to build our capacity to do more research and to do higher quality research at KSU. To achieve these objectives, we have designed internal funding programs with outcomes that go beyond helping faculty obtain pilot data for proposals. We have designed our internal funding programs to also serve as a form of professional development for faculty members to help them: 1) understand external sponsor priorities, 2) learn best practices for grant writing and proposal preparation, 3) build their skills of working in and leading teams, and 4) provide guided practice in project management.  This research project will assess whether our outcomes for internal funding align with our intentions. Our overarching research question is “Can internal funding be a tool to increase extramural funding and build research capacity by increasing so-called ‘soft skills’ among faculty researchers?” Students will assist with a literature review spanning education, psychology, marketing, and research development sources. They will assist with analysis of data we already have on external proposal submissions and awards plus new survey (and possibly interview) data we plan to collect. Students will help us create the surveys to assess faculty skills pre- and post- interaction with our internal funding mechanisms, and they will learn how to write and submit an Internal Review Board (IRB) protocol, after receiving training on working with human subjects. When our protocol is approved, students will support our data collection. Additionally, we anticipate students will make sufficient progress to present preliminary findings as a poster at the KSU Symposium of Scholars in spring 2027 and to help write sections of a peer-viewed publication for an article in Research Development Review: The NORDP Journal (e.g., significant parts of the introduction and methods sections and smaller contributes toward the results and discussion).

Malaria is one of the deadliest infectious diseases globally, causing hundreds of thousands of deaths each year. It is prevalent in many low- and middle-income countries and disproportionately affects young children under 5 years old. Insecticide-based interventions have been the main malaria prevention interventions. However, the widespread and increasing insecticide resistance poses a threat to effective malaria vector control. Our project aims to assess the spatial-temporal dynamics of malaria and investigate the impact of various mitigation strategies on the evolution of insecticide resistance in mosquito populations using (1) agent-based models (ABMs) and (2) systems of ordinary differential equations (ODEs). These two approaches complement each other. The student researchers can choose to work on either approach.  ABMs consider the individual behaviors of system components by defining a set of rules that govern how individuals interact on a spatial grid. This type of model relies heavily on probabilities, which allow for randomness of individual decision-making to be simulated. The ABMs will be built in NetLogo, a free open-source programming language and integrated development environment. The agents in our ABMs will have individualized characteristics. We will use the BehaviorSpace tool in NetLogo to specify the various parameter combinations (representing different intervention strategies) to simulate and the resulting outputs of interests. These simulations will aid in evaluating effective interventions for reducing disease burden in host population and insecticide resistance in vector population. Students do not need a background in calculus or linear algebra for the ABM approach.  ODE-based models excel at the formal analysis of population-level trends. The student researchers will modify and calibrate the ODE-based models that the PI’s group developed in the past. A combination of literature review and parameter estimation techniques will be used to determine appropriate parameter values for the updated model. We will analyze the model with global sensitivity analysis techniques such as Latin Hypercube Sampling and Partial Rank Correlation Coefficient to determine how parameters contribute to the quantities of interests (e.g., incidence, proportion of insecticide resistant vectors). The results of the sensitivity analysis will inform potential control strategies to mitigate disease spread and evolution of insecticide resistance in vector populations. The simulations and analyses will be carried out using MATLAB. Students do not need a background in differential equations for the ODE approach. Tutorials and sample code will be provided. Students do not need prior programming experience to be successful in this project, but it is a bonus.

This research project explores first-year students’ sense of belonging in college. A sense of belonging is the feeling of being valued, accepted, and connected to the campus community—socially, academically, and institutionally. It’s more than just being present; it’s about feeling like an important part of the college environment. Belonging can include having supportive relationships, feeling respected and recognized, and having access to the resources needed to succeed.   Research shows that belonging is a basic human need that plays a big role in student success. Students who feel they belong are more motivated, engaged, and likely to thrive. Those who do not often experience isolation, stress, and a greater risk of leaving college.   This mixed-methods project will examine belonging through surveys and focus groups. The University Student Belonging Scale will be used to measure changes over time, while focus groups will allow students to share their experiences in more detail. Together, these approaches will provide a clearer picture of how first-year students experience belonging at the university.   Through this project, First-Year Scholars will work alongside faculty and professional staff as co-researchers. You’ll gain hands-on experience with mixed-methods research, spending 5–10 hours per week learning how studies are designed, carried out, and shared. Key activities may include: • Reviewing research on student belonging • Analyzing survey data • Helping share the survey with students • Participating in focus groups with peers • Preparing a conference presentation on the findings • Contributing to a research paper for publication

This project, which is part of my research with CYPHR, explores how undergraduate students use generative AI while completing realistic workplace writing tasks. Surveys can show whether students use AI and how they feel about it, but surveys cannot fully reveal the decisions students make while writing. Think-Aloud Protocols (TAPs) make those decisions visible. During a TAPs session, participants explain their thinking as they plan, prompt AI tools, evaluate responses, verify information, revise drafts, and decide whether to accept or reject AI-generated material. The study will include 20 Kennesaw State University FinTech, Information Systems, and Cybersecurity majors. Participants will complete AI-supported writing activities involving financial products, digital payments, risk, compliance, data interpretation, and communication with clients or other nontechnical audiences. The research team will collect think-aloud recordings, AI interaction logs, drafts, and participant reflections. Researchers will examine how students check accuracy, verify sources, recognize bias, consider privacy, adapt writing for an audience, revise AI output, and maintain responsibility for their final work. First-year researchers are active members of the research team. With training and supervision, they will help prepare materials, assist with TAPs sessions, organize recordings and writing samples, transcribe selected sessions, and analyze data using a shared research codebook. They will compare coding decisions, discuss differences, identify emerging patterns, and help interpret what those patterns reveal about AI-assisted writing. All activities will follow approved procedures for informed consent, confidentiality, secure data handling, and participant choice. Through this work, they will gain hands-on experience with qualitative research, collaborative analysis, and professional research communication. The project will strengthen collaboration with colleagues in KSU’s Coles College of Business on WRIT 2010: Prompt Engineering for Business Writing, a required course in the B.S. in Financial Technologies. Coles faculty will help ensure that the study activities reflect disciplinary knowledge, workplace genres, professional expectations, and ethical challenges that FinTech students may encounter in advanced coursework and their careers. Findings will be used to improve WRIT 2010 assignments, prompt resources, evaluation rubrics, and ethical-use guidance. The project will show where students already demonstrate strong AI judgment and where they need additional instructional support. It will also produce a replicable TAPs research model, preliminary findings for future external funding, and evidence supporting the larger Prompting FinTech Success initiative. The project positions undergraduate students as knowledge makers, connecting their own learning with research that can improve AI education across KSU and beyond.

What if underwater robots could swim more like fish instead of relying only on rigid propellers and mechanical systems? This project explores the development of a bioinspired underwater robot that mimics the motion, flexibility, and adaptability of aquatic animals. Inspired by nature, the robot will use flexible structures, smart actuation, sensing, and intelligent control to achieve efficient and adaptive swimming behavior. Fish and other aquatic organisms achieve remarkable maneuverability and efficiency through continuous interactions between their flexible bodies and the surrounding water. Understanding this interaction is an important part of designing effective bioinspired robots. A major component of this project will therefore focus on fluid-structure interaction (FSI), exploring how the deformation of flexible fins and robotic structures influences water flow and, in turn, how fluid forces affect the motion and deformation of the robot. Students will investigate concepts such as fluid forces, flexible-body motion, thrust generation, and swimming efficiency from both mechanical and fluid-dynamics perspectives. Students will explore the project through complementary computational and experimental approaches. Computational work may include modeling the dynamics of the robot, simulating fluid-structure interactions, and studying how parameters such as fin geometry, flexibility, swimming frequency, and actuation patterns affect performance. Experimental work will involve designing, fabricating, and testing robotic prototypes using flexible materials, actuators, and sensors. Students may use cameras and sensor measurements to track swimming motion and compare experimental observations with computational predictions. The project brings together robotics, fluid dynamics, solid mechanics, biomechanics, sensing, and control, allowing students with different interests to contribute to a common research goal. Students will also experience how researchers from different engineering backgrounds collaborate to solve complex problems. Bioinspired underwater robots could support future applications in environmental monitoring, marine-life observation, underwater inspection, search and rescue, and autonomous ocean exploration. Through this project, students will contribute to the development of next-generation underwater robots that use principles learned from nature to move more effectively through complex aquatic environments.

Interested in neuroscience? Take a look at our projects!   Our first-year scholars will be working on two main projects. First, they will be working on a new project looking at the relationship among cognitive functioning, EEG (electroencephalogram – measures brain waves), and inflammation.  Students will get experience running participants using EEG, collecting saliva samples, and administering cognitive tasks. All students will work in the wet lab completing enzyme linked immunosorbent assays (ELISA) and quantitative polymerase chain reaction (qPCR) to identify gene variations (single nucleotides; SNPs) in the target genes related to inflammation.    The second project will examine the relationship among executive functioning, substance use disorder, adverse life experiences, inflammatory biomarkers, and single nucleotide polymorphisms. Students will work in the wet lab extracting DNA and performing qPCR on saliva samples.    First Year Scholars will work hands on with both professors and other student researchers. Both projects will involve weekly lab meetings to discuss the literature and project progress. All students will present their research projects at the at our Georgia Undergraduate Research in Psychology Conference and Symposium of Student Scholars.

Nature provides remarkable examples of structures that are lightweight, flexible, adaptive, and capable of producing complex motion. Human joints, animal limbs, tendons, plant stems, and other biological systems achieve controlled movement through the interaction of geometry, material properties, and structural flexibility. This project will investigate how these principles can be translated into bio-inspired compliant mechanisms and soft robotic components for next-generation assistive technologies, including exoskeletons, prosthetic devices, and orthoses. Traditional robotic and assistive devices often rely on rigid links, mechanical joints, bearings, and multiple assembled components. Although these systems can provide strength and precise motion, they may also be heavy, bulky, expensive, and uncomfortable for the user. Compliant mechanisms generate motion through elastic deformation rather than conventional rigid-body joints. Similarly, soft robotic systems use flexible materials and structures to safely interact with the human body. These approaches offer significant opportunities to create assistive devices that are lighter, quieter, more comfortable, and better able to adapt to individual users. First-Year Scholars participating in this project will explore biological structures and identify design principles that can inspire new assistive technology concepts. Students will learn how to use computer-aided design, engineering analysis, and additive manufacturing to create and evaluate compliant and soft robotic prototypes. Possible projects may include compliant joints for upper- or lower-body exoskeletons, flexible mechanisms for prosthetic feet or hands, variable-stiffness ankle-foot orthoses, tendon-driven rehabilitation devices, and wearable robotic components that support human movement. Students will participate in the complete engineering design process, including background research, concept generation, CAD modeling, material selection, 3D printing, assembly, experimentation, and design refinement. Depending on the selected project, students may also integrate sensors, motors, microcontrollers, or simple control systems to measure motion, force, deformation, or device performance. Experimental results will be compared with analytical or computational models to better understand how geometry and material properties influence the behavior of the mechanism.

Health disparities and gaps in care among Black women are well documented. However, what is less available, researched and published are ways in which Black women in the U.S. are maintaining health and wellness. While there is still significant work to be done related to increasing access to care, ensuring equitable health outcomes and decreasing the burden of death, as well as disease, no population should be limited to narratives exclusively focused on disadvantages and sickness. Without counternarratives, Black women are often reduced to stories of triumph, resilience, illness, struggle and disease burden.    To that end, the Black Women Be Well (BWBW) Lab is a student-engaged research endeavor focusing on exploring the narratives of various aspects of livelihood experienced by Black women related to health, wellness, and well-being. This is done via designing and implementing qualitative, as well as mixed-methods, research; disseminating findings across a wide array of academic and artistic mediums; and building a collection of stories for an edited volume.    The data from this research series are essential as they assist in informing scholars and practitioners of ways to enhance evidence-based, population health initiatives. Additionally, these counternarratives: provide roadmaps for women navigating well-being; add different perspectives to stories exclusively focused on illness and deficiencies; and assist in the enhancement of asset-identification among the population.     In the fall of 2025, the Lab launched with the first study focused on the lives of Black women between the ages of 35 – 50 who are single and do not have children. The results of that study will be published in two forthcoming manuscripts. Phase II of this research will focus on Black women and the outdoors, highlighting ways in which sports such as hiking, running, cycling and other outdoor movement are used to nurture well-being.    Each subsequent academic year will feature a different focus and topic, following a similar pattern of study design, data collection and analysis, as well as publications and presentations. Future topics may include, but are not limited to Black women and:     • Holistic health practices  • Healthy aging  • Caretaking and guardianship • Healthy relationships • And navigating mid-life transitions Students involved in the Lab are also invited to propose topics of interest, particularly those addressing AI use and health outcomes, as well as well-being among college students.

The BLINK (Behavioral Learning Investigation of Neurocognitive Effects of Cannabis) study will examine how cannabis use is associated with learning and memory using eyeblink conditioning, a well-established form of associative learning. Eyeblink conditioning provides an engaging behavioral model through which students can examine fundamental principles of learning while participating directly in the scientific research process. As cannabis use remains prevalent among young adults, understanding its relationship with learning and neurobiological function is particularly relevant to the college-aged population. This project will provide first-year scholars with meaningful, hands-on opportunities to participate in active human-subjects research. Students will receive training in research ethics and human subjects protections, participant recruitment and scheduling, data collection, and data management. Under supervision, students will assist with the eyeblink conditioning protocol and gain experience collecting, organizing, and preparing behavioral data for analysis. These experiences allow students to move beyond learning about research in the classroom and participate in the day-to-day process of conducting a scientific study. A central feature of the project is the opportunity to introduce students to molecular biology through the collection and analysis of biological samples. Students will be trained in foundational laboratory procedures, including biological sample handling, DNA extraction, and molecular assays. Depending on their level of preparation, students may gain experience with quantitative polymerase chain reaction (qPCR) and other techniques used to investigate genetic variation associated with behavioral and cognitive outcomes. Integrating behavioral and molecular approaches will introduce students to translational neuroscience research and demonstrate how biological and behavioral levels of analysis can address questions about substance use. Importantly, BLINK is designed as an entry point into a broader research training pathway within the Neurobiological Examination of Addiction, Recovery, and Related Disorders (NEARRD) Lab. Students who demonstrate continued interest and readiness may progress to increasingly independent human research responsibilities, advanced molecular biology techniques, and preclinical research. This progression can ultimately include training in rodent handling and behavioral procedures, allowing students to understand how human research findings can inform mechanistic animal studies.By engaging first-year students in authentic data collection and progressively introducing behavioral and molecular methods, BLINK provides a foundation for sustained research involvement. Students will develop technical skills, research literacy, and an understanding of translational science while contributing to an active research program.

The Department of Energy’s Vision 2020 report highlights the development of advanced catalysts as a critical scientific challenge. Catalysts are central to green and sustainable chemistry. They drive green and sustainable chemistry by speeding reactions without being consumed, enabling the production of medicines, plastics, fertilizers, fuels, and more. This project will explore a novel approach of producing transition metal-based catalysts through mechanochemical methods. Mechanochemistry offers solvent-free or solvent-minimized alternatives to traditional reactions, reducing environmental impact while often enhancing reaction efficiency, selectivity, and scalability. This project will help students understand modern green chemistry principles and expose them to cutting-edge research techniques. The student(s) will prepare new N-heterocyclic carbenes and their corresponding transition metals complexes and explore their function in catalysis.  The specific goals of the project are three-fold: 1) Synthesis and characterization of novel carbene ligands 2) Synthesis and characterization of the corresponding transition metal complexes via mechanochemistry 3) Investigation of the catalytic activity of these new complexes in several model reactions

What if artificial intelligence (AI) could look at a bridge photograph and recognize the structural components that an engineer sees? BridgeVision AI gives first year students the opportunity to explore this question using real bridge inspection images and modern computer vision. AI will be the main research tool. With step by step guidance from the faculty mentor, students will train a computer vision model to locate and identify structural components in bridge photographs. They will then investigate questions that do not have predetermined answers. For example, which structural components can AI recognize most reliably? Which components are more difficult to identify? What types of images cause the AI to miss a component or identify the wrong object? Students will compare results across the different bridge components and examine examples of both successful and unsuccessful predictions. No previous experience in programming, AI, or structural engineering is required. The faculty mentor will provide guided Google Colab notebooks and a prepared workflow so that students can focus on conducting research, understanding AI predictions, and interpreting results. By the end of the project, students will have trained and evaluated an AI model using real bridge images, produced visual and numerical results, and presented their findings at the KSU Symposium of Student Scholars. The project provides an accessible introduction to how AI and structural engineering can work together to advance bridge inspection.

Why does one heart pump normally while another struggles? Is it because of its shape, the thickness of its wall, the stiffness of its tissue, the strength of its contraction, or the pressure it must pump against? In this project, first-year students will investigate these questions using digital hearts: computer models that allow us to perform controlled experiments on cardiovascular mechanics. The project is connected to an ongoing NIH-funded research program in computational cardiovascular modeling and to collaborations with Emory University School of Medicine, including cardiovascular biomechanics researcher Hai Dong, PhD, and cardiac surgeon Bradley Leshnower, MD. Students will therefore work on an approachable part of a larger effort to understand cardiovascular disease using engineering, computation, and data. No previous research, programming, or biomechanics experience is required. Students will begin with a researcher-prepared digital-heart model and Python analysis tools rather than building a simulation program from scratch. Each student will become the team’s investigator for one research question. Up to five complementary research themes are available: • Heart Shape: How does ventricular size and shape affect pumping? • Wall Remodeling: What happens when the heart wall becomes thicker? • Tissue Stiffness: What changes when heart tissue becomes harder to deform? • Contractile Function: How does weaker or stronger contraction change cardiac output? • Pressure Load: What happens when the heart must pump against greater pressure? Students will make predictions, design virtual experiments, vary model parameters, and analyze quantities such as pressure, volume, deformation, stress, strain, stroke volume, and ejection fraction. They will create their own figures and determine which changes matter most. In the spring, the studies will be combined into a shared Digital Heart Dataset. The team will then ask a second question: Can an AI model learn the relationships that we discovered through mechanics? Students will train and test a simple predictive model using their own simulation data. By the end of the year, each student will have investigated an original research question, generated a dataset, created scientific figures, contributed to a team-level discovery, and presented the work at the Symposium of Student Scholars.

Have you ever wondered what happens behind the scenes when a product is created using a 3D printer? How much material does it use? How long does the process take? How much waste is produced when a print fails? Can real data help engineers make manufacturing more efficient and sustainable? This project gives first-year students the opportunity to explore these questions while helping establish a new additive manufacturing research laboratory. Additive manufacturing, commonly known as 3D printing, is changing how engineers design and produce products. Rather than cutting material away from a larger piece, additive manufacturing creates objects layer by layer. Although 3D printing can reduce material waste and enable innovative designs, understanding its real environmental and resource implications requires data collected directly from manufacturing processes. Students will work with the faculty mentor to help prepare and organize additive manufacturing equipment, learn how the machines operate, and conduct supervised experimental builds. More importantly, students will participate in a research investigation by collecting real data from the machines and manufacturing experiments. Depending on the capabilities of the equipment, students may record information such as build time, machine operating time, material input and consumption, material waste, unsuccessful prints, selected process settings, and other measurable indicators of manufacturing performance. Where feasible, students may also help collect information related to energy use. The research team will organize these measurements into a structured dataset and investigate patterns in how manufacturing conditions affect resource use and process performance. Students will learn how engineers transform observations from real machines into data that can be analyzed, interpreted, and used to answer research questions. The data collected through this project will support broader research on additive, hybrid, and sustainable manufacturing. Rather than relying only on assumptions or information reported in previous studies, the project will generate original experimental data from actual manufacturing operations. Students will therefore contribute to the development of a new research capability while gaining hands-on experience with 3D printing, experimentation, data analysis, and sustainability. This project is designed for curious students who enjoy building, experimenting, solving problems, and discovering what real-world data can reveal.

Modern cameras capture a sequence of images, or frames, at fixed intervals. This works well for everyday photography, but very fast motion can happen between those frames and may be missed or blurred. A newer type of sensor, called an event camera, works differently: instead of repeatedly taking complete pictures, it reports changes in brightness almost immediately as they occur. Because event cameras only report changes rather than continuously capturing full images, they can also operate with substantially lower data and energy requirements than traditional high-frame-rate cameras. This project will investigate the components needed to combine a conventional color camera and an event camera for future high-speed video reconstruction. Two first-year students will work on complementary parts of the system: one student will focus on the physical camera and data-collection system, learning Linux, operating the cameras, designing and 3D-printing a dual-camera mount, and developing recording software; the second student will focus on the computational system, learning Python, neural networks, public event-camera datasets, and existing methods for reconstructing video using conventional frames and event data. Because these are first-year students, the primary goal of this year will be to establish reliable and well-documented hardware and software systems rather than immediately integrating every component. Students will test their systems independently, document installation and operating procedures, record results and limitations, and prepare their work for presentation at the Symposium of Student Scholars. These foundations will support future students continuing the project into camera integration, original data collection, more extensive experimentation, and eventual scholarly publication.

What if an augmented or virtual reality system could recognize when a student needs additional guidance and adapt itself to provide personalized help? In this project, a team of First-Year Scholars will investigate how Artificial Intelligence (AI) and Extended Reality (XR) technologies can work together to create interactive systems that adapt to individual users. Rather than giving everyone the same information in the same way, we will explore interfaces that respond to factors such as a user's performance, behavior, attention, preferences, or surrounding context. Students will work together to design and build a functional intelligent XR prototype for a learning or training scenario. They will gain hands-on experience with modern XR research hardware available through KSU and the PIXR Lab and investigate how signals such as eye gaze, user performance, interaction behavior, or visual context can help an intelligent system decide when and how to adapt. Depending on the final research question, number of matched students, project progress, and available resources, the prototype may incorporate technologies such as eye tracking, computer vision, ML, or LLMs. Students will experience multiple stages of the research and development process: exploring previous research, identifying a research question, designing and implementing an XR experience, integrating intelligent technologies, testing and evaluating the resulting system, analyzing data, and communicating their findings. Team members can progressively develop expertise based on their interests, including XR/game development, applied AI/ML and intelligent adaptation, experimental design, data analysis, and scientific communication, while contributing to one shared research project. No previous research, XR, or AI experience is required. More important than prior experience are curiosity, initiative, reliability, willingness to learn independently, and enthusiasm for consistent contribution to the team. Students will receive guidance and learn the necessary tools throughout the project but should be prepared to contribute approximately 5-10 hours per week of research activity. Our primary goal is to finish the year with a functional intelligent XR system that students can demonstrate and evaluate at KSU's Symposium of Student Scholars. Depending on project progress and student interests, the prototype may also provide the foundation for a formal human-subject study, external presentation, future research publication, or continued research beyond the program. The project is intentionally designed to scale with the number of matched scholars: a larger team will allow us to explore additional sensing, intelligent adaptation, and evaluation components, while a smaller team will pursue a more focused version of the same direction.

Developing a new drug typically takes over a decade and costs more than a billion dollars. But some answers may already be sitting on pharmacy shelves. Drug repurposing means taking medicines already approved for one condition and discovering they also work against a different disease. Since these drugs are already proven safe, repurposing can bring treatments to patients much faster. Our project uses math and artificial intelligence to search for these hidden opportunities. Every drug works by binding to a protein in the body, like a key fitting a lock. We build computer models that learn what makes a good fit, then use them to scan libraries of approved drugs for unexpected matches to a disease-related protein. To do this, we represent proteins and drug molecules as networks: atoms become points, and chemical interactions become connections. In this project, we will use this approach to search for approved drugs that show promise against a specific disease target. No coding, chemistry, or biology background is required. We teach everything from the ground up, and welcome students curious about computing, medicine, or research.

City and regional agencies collect large amounts of information from transportation incident reports, road and transit records, 311 service requests, maps, and operational logs. Much of this information is difficult to use because it arrives as free text or inconsistent records. This project asks whether trustworthy artificial intelligence (AI) can turn these messy inputs into consistent, privacy-safe information that city staff and transportation planners could use in dashboards, planning models, and future digital twins, which are software representations of real-world systems. Students will help build and test a small prototype. Rather than asking an AI system for an unrestricted summary, we will require it to fill a fixed set of fields, such as event type, location context, transportation impact, or service-request category. Students will work primarily with publicly available, de-identified city-service and transportation records. If the core study is completed on schedule, the team may test a small set of open transportation images as an extension. Students will select and clean records, develop a labeling guide, create human-checked reference answers, run the prototype, and compare its outputs with those reference answers. Students will investigate which records the tool translates reliably, when it produces an invalid or unsupported answer, whether fixed output rules improve consistency, and what design changes reduce errors without hiding uncertainty. No prior research or advanced AI experience is required but highly encouraged. Training, starter code, and data templates will be provided. Students from any major who are curious about AI, cities, transportation, software, or public-service data are welcome.

Plastics are everywhere, from food packaging and electronics to household products, medical devices, and transportation. Although many plastics may look similar, they can have very different chemical compositions and physical properties. Correctly identifying plastics is important for recycling, quality control, and the development of more sustainable materials. However, real-world factors such as color, thickness, surface condition, and environmental aging can make plastic identification challenging. In this project, students will investigate a simple question: Can we identify different types of plastics by studying how they respond to light? When a material absorbs light, some of the absorbed energy is converted into heat. This produces a small but measurable photothermal response. Because different plastics absorb and convert light differently, these responses can provide characteristic “fingerprints” that may be used to distinguish one type of plastic from another. Students will work with common plastic materials and help build an experimental dataset of their optical and photothermal responses. The project will begin by measuring and comparing different polymer types to establish baseline signatures. Students will then investigate how real-world variations, such as color and thickness, influence these measurements. Depending on project progress, students may also examine how environmental exposure or accelerated weathering changes the response of selected plastics. Students will participate in the full research process, including developing research questions, preparing samples, conducting experiments, collecting and organizing data, and interpreting results. They will also learn basic data visualization and analysis techniques and explore whether measured patterns can be used to identify unknown plastic samples. No previous research experience is required. Students will receive training in laboratory safety, experimental methods, instrumentation, data analysis, and scientific communication. By the end of the project, students will have contributed to an original experimental dataset and will present their findings at the KSU Symposium of Student Scholars. This project is especially suitable for students interested in chemistry, materials science, engineering, environmental science, sustainability, or data analysis.

Artificial intelligence tools such as ChatGPT are increasingly being used to answer questions about cybersecurity. An everyday user might ask, “What is a passkey, and is it safer than a password?”, “Is this email a phishing attempt?”, or “What should I do if one of my accounts is hacked?” A software developer might ask AI how to securely configure a website, protect user information, or fix a security problem. AI can make cybersecurity information easier to access and understand. However, cybersecurity advice can have real consequences. An answer that sounds convincing but is incomplete or incorrect could encourage someone to make an unsafe decision. This creates an important question: How reliable is AI when people turn to it for cybersecurity advice? In this project, students will help investigate how well AI systems answer real-world cybersecurity questions. The project will begin with an accessible data-collection phase. Students will collect and organize publicly available cybersecurity questions from sources such as online discussion forums, educational websites, security resources, and common real-world scenarios. Questions may cover topics such as passwords and passkeys, phishing and scams, account security, online privacy, mobile applications, web security, and secure software development. Students will then work with the research team to develop a systematic way to evaluate AI-generated answers. We will examine questions such as: • Is the AI's answer factually correct? • Is important security information missing? • Is the explanation understandable to someone without a technical background? • Could following the advice create additional security or privacy risks? • Does AI perform differently when answering questions from everyday users compared with questions from developers or IT professionals? As the project progresses, students may also explore whether different ways of asking questions, providing additional context, or giving AI feedback can improve the quality of its answers. No previous programming, cybersecurity, or artificial intelligence experience is required. Students will learn the necessary concepts as part of the research experience. The project is designed to begin with straightforward activities such as finding, reading, organizing, and categorizing information, and gradually progress toward evaluating AI systems, analyzing data, and communicating research findings.

Artificial intelligence (AI) tools are becoming increasingly capable, but even the most advanced AI systems can still become confused, reason incorrectly, or respond in unexpected ways. This project asks a simple question: Can you find creative ways to make AI fail?   Students will join an active research team (4 KSU faculty and 9 KSU students) studying how generative-AI responds to challenging or unusual input. The project will introduce students to AI evaluation, red teaming, and adversarial prompting, which are methods researchers use to identify weaknesses in artificial intelligence systems and improve their safety and reliability. Students will begin by learning how modern AI systems work and reviewing examples of techniques that previous researchers and students have used successfully to make AI fail. They will experiment with several advanced AI models (e.g. OpenAI’s GPT-5.6, Google’s Gemini 3, DeepSeek R1, Qwen 3.8, Anthropic’s Claude Sonnet/Opus, etc.)  and learn how models respond to text-based and multimodal prompts that combine text, images, and other information. After building this foundation, students will develop and test their own prompting techniques. The goal is to identify new methods that cause AI systems to reveal limitations in their reasoning or otherwise behave in unusual and measurable ways that expose AI weakeness. Students will generate original experimental data by systematically testing their approaches across multiple models, documenting model responses, and comparing which techniques work most consistently across models.   No previous AI research or programming experience is required. Students from any major who are curious, creative, persistent, and interested in understanding how artificial intelligence works are encouraged to apply. Students interested in data science or programming will have opportunities to learn more advanced techniques, including coded experiments and API-based model testing. Students will become members of the Data Quality and Survey Methods Lab and work with faculty and student researchers on a larger program of AI intersecting human research. Results will be presented at KSU's Symposium of Student Scholars and Analytics Day, with opportunities to contribute to conference abstracts, undergraduate research conferences, national or international presentations, and future publications.

Researchers who study how the brain generates movement frequently use the fruit fly as a model organism. Its nervous system is compact enough to be surveyed in its entirety, yet it produces behavior that is remarkably rich and variable. Recording neural activity in this animal, however, imposes a mechanical constraint. Conventional preparations tether the fly so that its head and thorax are held motionless while its legs remain free to move on an air-supported or motor-driven ball. Immobilizing the head is what makes fluorescence microscopy of the brain possible, because the imaging optics must stay fixed relative to the tissue. The approach is productive, but it leaves a question that has never been carefully measured: does a tethered fly still behave the way an unrestrained one does? This project characterizes an instrument designed to address that question. A fly walks on top of a sphere while two motors beneath the sphere rotate it in the direction opposite to the fly's motion, and a camera continuously locates the animal so that a computer can command the rotation needed to return it to the top. The fly is never tethered or immobilized; it simply walks while the surface moves beneath its feet to keep it within view. Students will characterize its performance, develop a model of its motion, and then use it to measure how fruit flies actually walk for kinematic analysis.  The project has three objectives. First, students will characterize the system response, determining how commanded motor rotation maps onto sphere rotation, and will use those measurements to build a motion model. Second, students will compare walking on the instrument with walking in a stationary arena with non-tracking and tracking cameras. Lastly, students will analyze and compare the behaviors on different planforms using measures such as speed, turning rate, path shape, and how long a fly walks before it pauses. Because video of a rapidly moving insect is impractical to analyze by hand, we will use machine learning to locate the animal and its body orientation automatically. Students will annotate flies in video frames, and then train and test the developed system. Students will also test whether software that anticipates the fly's next position improves the instrument's ability to keep pace with it.

Critical patient-nurse interactions, such as delivery of patient education and discharge instructions, have been shown to impact patient health outcomes, satisfaction, and ultimately the likelihood of a patient being readmitted.  At the same time, virtual nursing care models, such as telehealth, have gained substantial traction in hospitals by addressing staffing shortages while improving operational efficiency and reducing costs.  Despite the efficiency gains, there is little research examining how virtual nursing affects patient-nurse communication compared with traditional bedside encounters.  This research seeks to understand how patient interactions differ between bedside and virtual nursing encounters and will help healthcare systems better understand the tradeoffs between bedside and virtual nursing care. The research applies a human factors and systems approach to evaluating bedside and virtual nursing encounters through multiple complementary measures, including attention, communication behaviors, cognitive workload, emotional response, and patient perceptions of care.  Research Questions: • How do patient attention, engagement, comprehension, and communication behaviors differ between nurse-delivered bedside and virtual education? • How do patient cognitive workload, emotional response, and perceptions of care differ between nurse-delivered bedside and virtual education? The present research builds upon an initial study conducted during the 2025-2026 First-Year Scholars Program, under the direction of Dr. Awatef Ergai (a co-PI for this project).  The initial research compared virtual and bedside discharge education for 14 patients who were hospitalized for a variety of conditions and established the feasibility of using eye-tracking with hospitalized patients.   The current research for 2026-2027 expands this methodology using a more consistent patient population and educational content.  We have established a relationship with Kennestone Hospital in Marietta, Georgia, to work exclusively with cardiac patients during their inpatient hospitalizations. Cardiac patients routinely receive extensive education and discharge instructions, making them ideal participants. A key component of this research is the use of eye-tracking tools to measure visual attention during bedside and virtual nurse encounters.  Eye-tracking provides objective measures of engagement, visual processing, and cognitive workload.  Additional data will be collected using vocal analysis, observations, surveys, and other measures to assess the patient experience during nurse encounters.

How can a wearable device understand what a person intends to do with their hand without relying on adhesive or conductive electrodes attached to the skin? This project will explore a new approach that uses low-power radio-frequency (RF) signals to sense changes in the forearm caused by muscle contraction and hand movement. When muscles contract, their shape and internal tissue properties change slightly. These changes can alter how radio waves travel around and through the forearm. By placing a small transmitting antenna and receiving antennas near the arm, we will investigate whether these RF changes can be measured and used to identify movements such as making a fist, opening the hand, or moving the wrist. Unlike conventional electromyography (EMG), which measures electrical muscle activity through skin-contact electrodes, the proposed approach does not require conductive electrode-to-skin contact. If successful, it could provide a comfortable complementary sensing method for future prosthetic hands, rehabilitation systems, wearable health devices, and human-computer interfaces. First-Year Scholars students will help build and test the sensing setup using programmable radio hardware, antennas, and motion sensors. Students will learn how to collect synchronized RF and hand-motion data during controlled experiments, visualize the signals, investigate how different movements affect the measurements, and identify useful patterns. In the second phase, the team will apply basic signal-processing and machine-learning methods to determine whether the RF measurements can distinguish several hand and wrist movements. The project is intentionally designed so that students do not need previous experience in biomedical engineering, radio systems, or artificial intelligence. Students will receive step-by-step training while contributing to a real research question whose answer is not yet known. The long-term goal is to determine whether RF sensing can become a practical wearable technology for recognizing muscle activity and motor intent.

Restaurant franchises may look similar from the outside, but the rules behind each brand can be very different. Some franchisors tightly control suppliers, products, technology, and operating procedures. Others give franchisees more room to make decisions. Some contracts also give franchisors broad authority to change rules as markets and technology evolve. This project asks the following question: Which combinations of control, flexibility, and experience are associated with stronger restaurant franchise performance? Student scholars will work with Franchise Disclosure Documents (FDDs), which are legal documents that U.S. franchisors provide to prospective franchisees. We will focus primarily on 2017 FDDs for restaurant chains and combine them with an existing FRANdata dataset that tracks chain characteristics and performance from 2016 through 2019.The preliminary FRANdata sample contains 73 restaurant chains, and our current FDD availability inventory identifies a 2017 FDD for 50 of those chains. Because some documents may be incomplete or unsuitable for analysis, the final matched sample will depend on usable FDD availability and is expected to include approximately 45 to 50 restaurant chains. A pilot study using 12 restaurant chains confirms that these sources can be matched and that the contract language varies meaningfully across brands. Students will learn to turn large amounts of text into usable research data. They will identify and code language related to operating control, the franchisor’s ability to adapt rules, and how specifically contract requirements are stated. Computer-assisted text analysis will then be used to compare patterns across brands. We will link these measures to profitability outcomes, including return on assets measured as net income relative to total assets, profit margin, and related chain-level profitability measures. The final stage will use fuzzy-set Qualitative Comparative Analysis, a method designed to identify more than one “recipe for success.” Instead of assuming that one practice works for every brand/chain, the study will examine whether different combinations of control, flexibility, contract design, and system experience can lead to strong financial performance. Students will contribute directly to data collection, coding, analysis, interpretation, and presentation of the findings.

Modern engineering systems—including aircraft, biomedical implants, power plants, marine structures, and transportation infrastructure—depend on metallic materials that must withstand harsh service environments for many years. Corrosion, the gradual deterioration of metals due to chemical and electrochemical reactions, is one of the leading causes of structural failures and economic losses worldwide. As advanced manufacturing technologies continue to evolve, engineers must understand how newly developed materials perform in corrosive environments. This research project investigates the corrosion behavior of metallic components produced using Wire Arc Additive Manufacturing (WAAM), an emerging large-scale metal 3D printing technology. Unlike conventional manufacturing, WAAM builds components layer by layer using welding wire and electric arcs, enabling the production of complex parts while reducing material waste and manufacturing costs. Although WAAM has significant advantages, relatively little is known about how multi-material components produced by this process resist corrosion under real-world conditions. Students participating in this project will investigate how different engineering alloys and multi-material structures behave when exposed to saltwater environments that simulate marine and industrial service conditions. They will prepare metallic specimens, perform electrochemical corrosion testing, observe corrosion damage using optical and scanning electron microscopy, and analyze experimental data to understand how microstructure influences corrosion resistance. No prior research experience is required. Students will receive individualized mentoring and hands-on training in laboratory safety, materials characterization, corrosion science, data analysis, and scientific communication. Throughout the project, students will participate in weekly research meetings where they will discuss experimental results, interpret data, and learn how engineers solve real-world materials challenges. Rather than completing a predetermined laboratory exercise, students will contribute to an active research program investigating corrosion behavior in additively manufactured multi-material systems. Their experimental observations will help improve understanding of advanced manufacturing materials that may eventually be used in aerospace, energy, transportation, and biomedical applications. At the conclusion of the project, students will present their research findings at the Kennesaw State University Symposium of Student Scholars. Outstanding work may contribute to presentations at regional or national conferences and future peer-reviewed publications.

Cosmetic surgeries, Botox, dermal fillers, liposuction, and other body-enhancing procedures bring in massive amounts of revenue. The global demand for cosmetic surgeries has risen over the past decades (Triana et al. 2024). In the United States the rise can be linked to direct-to-consumer media advertising that constructs appearance as a medical problem.  In this FYS project, we will examine the rise of cosmetic procedures in the US within the framework of medicalization, defined as the processes by which non-medical problems such as natural aging become medical issues under medical control. We will also examine beauty ideals through the lenses of gender, culture, and history and how the cosmetic procedures industry evolved.  We will begin by reading the foundational text, Cosmetic Surgery: The Cutting Edge of Commercial Medicine in America, written by medical sociologist Deborah A. Sullivan. This provides a launching point for historical references, theoretical frameworks, research projects, and further analyses. Other peer-reviewed articles and informational health briefs will be discussed, tailored to student’s engagement with key concepts. During the experience, students will explore the commercialization of cosmetic procedures.   Possible projects could include: • Review men’s and women’s magazines for articles and/or advertisements for cosmetic procedures. Identify key themes. What messages do the articles and advertisements convey about outcomes, appearance, risk, gender? • Design an interview guide to ask participants about cosmetic procedures. Topics could include satisfaction, complications, cost, or other issues.  • Perform a content analysis of movies and television programs that feature cosmetic surgery. What messages are these media representations sending to potential consumers? References Triana L, Palacios Huatuco RM, Campilgio G, Liscano E. “Trends in Surgical and Nonsurgical Aesthetic Procedures: A 14-Year Analysis of the International Society of Aesthetic Plastic Surgery-ISAPS.” Aesthetic Plast Surg. 2024;48(20):4217-4227. doi:10.1007/s00266-024-04260-2 Sullivan, Deborah A. 2001. Cosmetic Surgery: The Cutting Edge of Commercial Medicine in America New Brunswick, NJ: Rutgers University Press. ISBN-13 978-0813555850

Einstein called quantum entanglement “spooky action at a distance.” Could this strange quantum effect be created across the 700 miles between Atlanta and New York? Quantum entanglement happens when two particles behave as parts of one shared system, even when they are far apart. It is one of the most surprising ideas in quantum science and an important part of research on quantum computers and the future quantum internet. Creating entanglement in a laboratory is one thing. Creating it between two distant cities is much harder. The farther the distance, the more likely the particles are to be lost or disturbed. Quantum repeaters may help by creating entanglement over shorter sections and then connecting those sections. More repeaters can shorten each section, but every repeater also adds new operations that may fail. This leads to an open question: How many repeaters would actually help? Students in this project will work as a team to investigate this question using simulation data. They will look for the point at which entanglement becomes too weak or unreliable and test whether repeaters can push that limit farther. Using Python, students will compare results, find patterns, and create graphs that show what they discover. No previous experience with quantum science, programming, or data analysis is required. Students who are curious about quantum technology, enjoy working with numbers, or like finding patterns are encouraged to apply. During the project, they will learn the basic ideas of quantum entanglement and gain practical experience in data analysis, teamwork, and scientific research. At the end of the year, the team will present its answer to the Atlanta to New York question at the Symposium of Student Scholars.

What does it actually take to succeed at work? Not just doing your job well, but knowing how to speak up in a meeting, build the right relationships, read the unwritten rules of an organization, and feel like you belong in professional spaces. Most of this isn't taught in any classroom, yet it can shape an entire career. This gap matters for everyone entering the workforce, but it can hit hardest for first-generation students and others without insider access to professional norms and networks, a question that feels especially urgent here at Kennesaw State, where so many students are the first in their family to pursue a college degree and where career success shouldn't depend on who you already know. This project investigates how professionals across public service, nonprofit, and private-sector organizations actually learned these unwritten rules, and what helped or hindered them along the way. Working directly with the faculty mentor, the First-Year Scholar will help conduct in-depth interviews with professionals at different career stages, from recent graduates to seasoned leaders, exploring their experiences with workplace culture, professional relationships, and career advancement. The Scholar will receive training in qualitative research interviewing, learn how to build rapport and ask thoughtful follow-up questions, and gain firsthand exposure to careers across multiple sectors, an invaluable head start for a first-year student still exploring career paths. Beyond developing research skills, the Scholar will build a network of professional contacts and gain insight into workplace dynamics well before their peers. No prior research experience or specific major is required, just curiosity about how people build successful careers and comfort having conversations with professionals. This project is well suited for a First-Year Scholar interested in psychology, business, communication, sociology, political science, public administration, or simply figuring out their own future career path. By the end of the year, the Scholar will have contributed to original research, presented their work at the Symposium of Student Scholars, and gained a professional network and skill set that most students don't develop until much later in their college career.

As an interdisciplinary project, this research may interest students in gender studies, sociology, communication, art, museum studies, and media studies. Through the First-Year Scholars Program, students will develop skills in qualitative and content analysis, grounded theory, and gender studies. Students will engage with content analysis, interview and human subject training, transcription, qualitative analysis, and will develop an individual small-scale research project with faculty mentorship for presentation at a regional academic conference. The project explores masculinities within traditionally feminine spheres of fiber arts, quilting, textiles, and needlecraft. It will examine representations and experiences of men across multiple media, including quilting magazines, instructional videos, social media, and other forms of fiber-art communication, as well as interviews. Given the visibility of queer and gay men in fiber arts, the project may also explore this area in part. A central component will be ethnographic research at the Modern Quilt Guild’s national show, QuiltCon 2027, in Atlanta (February 25–28, 2027). “Quilts” in the United States are commonly understood as pieced, layered, and stitched bedding or decorative objects, while quilting and many fiber arts are practiced primarily by women. Nevertheless, men have long participated in quilting and needlework, as well as the commercial, educational, and artistic communities surrounding these practices (McBrinn 2016; 2021) (for a good media example of the potential breadth of topics and forms of art, the magazine “Fiber Arts Now” https://www.fiberartnow.net/ may be of interest).. The project will examine how men enter and navigate a predominantly female social sphere, including how they negotiate their identities when they become “open” about being quilters and how participation in quilt shows and exhibitions may influence identity and socialization.  Students will contribute to literature and media research, analyze representations of quilting and men in fiber arts, and assist in locating male quilting artists for faculty-conducted interviews (IRB-FY24-583). Students will not conduct interviews but may assist with transcript review, coding, and qualitative analysis. Given the limited scholarship on men and quilting, this project offers an opportunity to expand understanding of masculinity, gender, identity, and artistic practice. Students will synthesize academic and online resources, conduct qualitative media analysis, participate in field observation and ethnographic research, and contribute to the analysis of faculty interviews. The project will culminate in an individually selected research topic, with the goal of developing a poster presentation for a regional sociology or related academic conference.

Project Overview This project investigates how specific parental support within U.S. Latinx and African American households buffers the intergenerational transmission of math anxiety. As families enter public schools, children face academic expectations that can trigger mathematics avoidance and stress. While historical literature applies a deficit lens to minoritized caregivers, this research adopts an asset-based framework. It examines how cultural strengths—such as familismo (collective family academic support) and positive African American mathematics socialization—serve as protective shields, mitigating the transmission of a parent's math anxiety as children adapt to institutional, systemic, and linguistic environments. Brief Literature Review A parent’s math anxiety (MA) directly predicts a child's achievement, self-concept, and anxiety through intergenerational transmission. This affective drop is heavily exacerbated during stressful homework interactions. The disruption account reveals that anxiety actively co-opts working memory resources, rather than tracking poor innate abilities. Caregivers' rigid "procedural" histories trigger frustration when navigating modern standards-based elementary curricula. However, this trajectory can be buffered through "academic socialization". Structuring the home math environment through informal play-based discovery and utility value conversations fosters mathematical resilience and a positive math self-concept. These interactions provide vital socio-emotional buffering without requiring technical instructional expertise. To answer the research question, “In what ways do culturally specific parental support practices buffer the transmission of math anxiety among Latinx and African American families navigating the transition into the US public school system?”, the research team will survey and interview families. Significance Grounding this study within the U.S. public school infrastructure transforms how educators collaborate with minoritized families. Systemic school structures create a "home-school divide" when caregivers' backgrounds clash with modern, reformed math pedagogy. Relying on rigid worksheets can inadvertently trigger a parent’s math anxiety, turning home learning into a space of frustration. By adopting an asset-based framework, this project reframes minoritized home math environments as spaces rich with cultural strengths. Latinx familismo activates extended family networks for academic support. Similarly, positive African American mathematics socialization serves to foster mathematical resilience and shield children from external, racialized STEM stereotypes. These practices solidify academic socialization, where a family's value system, warm interactions, and conversational scaffolding matter far more than technical instructional expertise. Uncovering these dynamics provides critical insights for schools navigating structural math declines. Ultimately, this project moves public education toward culturally responsive initiatives. It equips educators to meet families within their unique cultural funds of knowledge, leveraging home-based strengths to protect early mathematical resilience.

My lab is developing an AI-powered drone platform to support public safety activities such as disaster response, infrastructure inspection, hazard identification, and search operations. The goal is to create a drone that can do more than simply capture images. By using onboard artificial intelligence, the drone can help recognize important objects or conditions, respond to changing situations, and provide useful information to people making decisions on the ground. We have already developed an initial drone prototype and an early version of the supporting software. The next stage of the project will focus on improving the system, integrating the hardware and software, testing its performance, and preparing demonstrations. First-year students will work directly with me and members of my research lab on hands-on tasks that may include assembling and modifying drone components, connecting cameras and onboard computers, testing software functions, collecting and organizing experimental data, documenting results, and helping prepare indoor or outdoor demonstrations. Students may also contribute to testing AI functions such as object recognition, hazard detection, damage assessment, and automated mission reporting. No previous experience with drones or artificial intelligence is required. Students should be curious, willing to learn, comfortable working in a team, and interested in technology with real-world impact. Training and close guidance will be provided throughout the project. By participating, students will gain introductory experience in drones, artificial intelligence, computer vision, embedded computing, software testing, and engineering design. They will also learn how a research idea develops into a working prototype and how researchers evaluate whether a technology is reliable and useful. At the end of the project, students will help present an improved prototype and demonstrate how an AI-powered drone could support future public safety missions.

For people with mobility challenges caused by conditions such as cerebral palsy, spinal cord injury, or knee osteoarthritis, walking can require much more effort than it does for most people. Wearable robotic exoskeletons have the potential to help by providing additional support and assistance at the knee. However, many existing exoskeletons are bulky, heavy, expensive, and designed for a limited range of users. What if we could design a lighter, more comfortable, and more personalized knee exoskeleton from the ground up?   In this project, students will take on that challenge themselves. Rather than modifying an existing brace or following a predetermined mechanical design, the student team will create an entire knee exoskeleton mechanical system from a blank page. You will decide how the exoskeleton should attach to the leg, how its structure should move with the knee, where the actuator should be placed, how forces should be transmitted, and how the design can remain lightweight, comfortable, safe, and easy to use.   The electronic hardware, sensors, actuator, and basic control platform will be provided by our research lab as a starting point, but students are encouraged to improve them. Mechatronics students may redesign electronics, develop custom PCBs, integrate sensors, and learn how to drive and control motors on a real robotic system. Students focusing on mechanical design can use CAD software, 3D printing, machining, and rapid prototyping to design, build, test, and refine the exoskeleton into a functional wearable prototype.   Students interested in AI, computer engineering, programming, and controls can also develop the “intelligence” and new functions of the robot. Depending on their background and interests, they may implement an existing control method or design and test their own algorithms for movement recognition, assistance control, or other functions. Even when using an existing method, students will gain hands-on experience implementing algorithms on a physical knee exoskeleton and understanding how software interacts with hardware.   By the end of the project, the team will have created a complete knee exoskeleton prototype that can be worn, operated, and experimentally tested. Students will then collect and analyze performance data to answer research questions such as how well the device follows human movement, how much assistance it can provide, and how different design choices affect its performance. This project gives students the opportunity to experience the entire engineering research process—from an initial idea to mechanical design, to a physical robot, and finally to experimental results.

Artificial intelligence (AI) is becoming part of how children learn, communicate, and create. This project explores how we can design AI technologies and learning activities that help K-12 students understand AI, create with AI, and think critically about how AI works.  As a First-Year Scholar, you will join a research group investigating how we can design engaging and responsible AI learning experiences for K–12 students. You will have opportunities to help design AI technologies and learning activities, work with K-12 students, collect and analyze research data, and share what you discover. Dr. Tian has extensive experience engaging young people in learning about AI by creating their own conversational agents. Her research explores how children can move beyond simply using AI to become creators who understand how AI works, make design decisions, and think critically about AI.  You will work with two existing educational technologies developed by Dr. Tian and her research collaborators: AMBY and LUMI BotBuilder. These platforms allow young learners to create and customize their own conversational AI agents while learning important AI concepts. As part of the project, you may test and improve these technologies, brainstorm new features, and help design hands-on learning activities for middle school students. You will also participate in researcher-supervised educational activities with K–12 learners and help investigate questions such as: How do children design their own AI agents? What do they understand about how AI works? What challenges do they encounter? How can we design better technologies and activities to support their learning? Throughout the year, you will learn how to develop research questions, work with research data, identify meaningful patterns, and communicate your findings. By the end of the project, you will develop and present a research poster based on your work. No previous research, programming, or AI experience is required. Previous experience working with K-12 students, such as through teaching, tutoring, summer camps, or other youth programs is preferred. If you are curious about AI, education, technology, or working with young learners, this project will provide an opportunity to gain hands-on research experience.  Learn more about Dr. Tian's research: txiaoyi.com

Memorials are places where people remember, grieve, reflect, and seek meaning after traumatic events. Architects carefully consider how these places look and feel, but there is still much to learn about how people actually experience them. This research will investigate whether principles of Trauma-Informed Design can help us better understand and evaluate memorial spaces. Students will begin by studying approximately ten nationally significant memorials, including the Oklahoma City National Memorial, the September 11 memorials in New York, Arlington, and Shanksville, the National Memorial for Peace and Justice in Montgomery, Alabama, and the developing National Memorial of the Underground Railroad. Students will examine what architects have written about these places and identify design approaches intended to support reflection, dignity, safety, connection, and healing. The project will then move beyond reading and observation. Students will help develop an Institutional Review Board (IRB) proposal and research questions for a study involving people who design memorials and professionals working at the forefront of Trauma-Informed Design. Following IRB approval, students will participate in interviews with memorial architects and with psychologists and other professionals who study trauma and healing. This project is intentionally interdisciplinary. Architecture can shape the physical environment, but understanding healing requires perspectives beyond architecture. Students will have opportunities to learn from and engage with architects, psychologists, social workers, and nurses, bringing together different ways of understanding human experience. Students will work directly with the faculty researcher and may also connect their work to ongoing undergraduate thesis and studio research. By the end of the project, students will have contributed to a study that seeks to generate new knowledge, not simply summarize existing research. They will present their findings at Kennesaw State University's Spring Symposium of Student Scholars and may have opportunities to continue the research into future courses, thesis projects, conferences, or publications.

Generative world models and 3D artificial intelligence tools are rapidly changing how virtual environments are created. From a short text prompt, image, or video, these tools can produce navigable 3D scenes and objects in minutes. Their outputs may take the form of traditional 3D meshes or “Gaussian splats”, collections of colored 3D points that can create highly realistic views. However, most generated assets are designed mainly to be seen. The XR system may not know that an object is a door, medical instrument, control panel, or safety hazard, or which parts should move and how they should respond to a user. This project will explore how to turn these visually rich but mostly inert assets into understandable and interactive virtual environments. We will investigate a pipeline in which a vision-language model (VLM), an AI system that connects visual information with language, examines generated 3D assets, recognizes objects and their parts, and attaches meaningful labels and possible actions. For example, the system might identify a cabinet door and suggest that it should open, or recognize a fire extinguisher and connect it to the appropriate steps in a safety exercise. Because AI can make mistakes, the project will also develop a human-in-the-loop workflow. A person will review, correct, and refine the AI’s suggestions and determine which interactions fit the purpose of each experience. We will study how this human-AI collaboration affects authoring time, usability, trust, workload, and the quality of the final interactions. The project will also investigate 3D user-interface designs that help creators inspect assets, assign behaviors, and understand the AI’s recommendations within an immersive environment. By making generated environments interactive and easier to customize, this work could reduce the time and specialized labor required to build interactive and immersive virtual training and simulations, support rapid creation of scenario variations, and provide learners with safe opportunities to practice consequential tasks.

Nature uses fundamental molecules, such as amino acids and DNA/RNA, to build complex structures, including proteins, membranes, and viruses. Inspired by nature, our lab explores peptoids—synthetic biomimetic molecules that can be designed to self-assemble into highly functional and crystalline nanoscale supramolecular polymeric materials with applications in biomedicine, nanotechnology, materials science, and environmental remediation.  In this project, we will investigate how changes in the shape and structure of individual peptoid molecules affect their interactions with one another, thereby building larger, more ordered structures. We will focus on comparing straight-chain peptoid molecules with peptoids whose ends are connected to form a small ring and study how these changes affect the formation of nanoscale tubes, fibers, helices, sheets, and other highly functional ordered nanomaterials. Additionally, these materials will be stimuli-responsive, undergoing structural and functional changes in response to stimuli such as light, heat, and mechanical force. Students will gain hands-on experience in organic synthesis, peptoid synthesis and purification, spectroscopy, and nanomaterial characterization. They will use and become familiar with techniques such as NMR, UV–visible spectroscopy, FTIR, atomic force microscopy, and scanning electron microscopy to study small molecules and materials ranging from individual molecules to systems thousands of times larger than the individual molecules.

Research Problem Computational thinking (or CT) is the ability to break a complex computational problem into manageable parts, recognize patterns, design step-by-step solutions, and test and improve those solutions in language that is understandable by computers. These skills are valuable across computing and STEM, but learning them through programming can be frustrating when students receive either too little guidance or complete answers that remove the opportunity to think. Further, CT learning and education are now fundamentally challenged by generative AI, and we need novel and more engaging learning tools to involve young students in CT learning and development. Solution To further engage young students with CT learning and development, this undergraduate research project will develop and study a gamified, AI-powered learning environment designed to help young learners create Scratch projects while gradually becoming more independent problem solvers. The environment will combine Scratch—a low-entry coding tool used by million of young students to learn coding worldwide—; Dr. Scratch—a tool that analyzes Scratch projects—; and a team of intelligent mentors embedded in Scratch as AI copilots. Each AI mentor will have a distinct personality and teaching role. CT-Buddy will provide frequent encouragement and targeted hints for early debugging. CT-Partner will guide students in coding but without providing hints and by asking reflective questions. Finally, CT-Guru will introduce advanced challenges while offering minimal assistance to preserve students' productive struggle. Similar to climbing a challenge tower in a video game, learners will face increasingly difficult tasks as support gradually fades. Set-up The First-Year Scholar will join an emerging research lab and collaborate with the faculty mentor, a Ph.D. student, and a software developer (a former KSU alumnus). The scholar will help examine previous research; design mentor behaviors, challenge levels, and game features; program and test the prototype; document development decisions; and participate in a small-scale research evaluation. Following university ethics and Institutional Review Board (IRB) approval, the team will examine usability, learning performance, interaction logs, and participant feedback to understand how different levels of AI support influence persistence, reflection, and independent problem-solving. The project is well suited to a versatile student interested in artificial intelligence, programming, games, education, or research. Experience or strong interest in Python or related languages, AI tools such as Gemini, GitHub, and cloud platforms is helpful. The project will produce a working prototype, an analyzed dataset, and findings for presentation at the Symposium of Student Scholars and potential future publication.

Food insecurity remains one of the most pressing global challenges, affecting billions and undermining human capital, economic productivity, and child survival. Yet, while vast amounts of relevant data exist, they remain fragmented across multiple international sources—including FAOSTAT, the World Bank, and the Institute for Health Metrics and Evaluation—with no centralized platform to integrate, visualize, or simulate policy-relevant scenarios. This project addresses that critical gap. I propose to develop an Interactive Data Platform for Food Insecurity Analysis that combines data curation, econometric analysis, and dynamic visualization into a coherent "data story." The platform will be built around the four core pillars of food security—Availability, Access, Utilization, and Stability—and will allow users to explore these dimensions through both static and interactive content. Static elements will include definition boxes explaining key indicators, curated case studies illustrating real-world applications, and expert video contributions to contextualize the data. Interactive features will offer significantly more analytical power: world maps displaying selected indicators over time, line charts tracking country or regional trends, and scatter plots comparing variables—most notably against GDP per capita to expose relative strengths and vulnerabilities. The platform’s most innovative component, however, is its simulation engine. Drawing on prior econometric groundwork, the tool will enable users to model the downstream effects of improved food security on three crucial outcomes: GDP per capita, child mortality rates, and human capital accumulation. Users will be able to adjust policy levers—for instance, reducing food insecurity by a given percentage—and immediately visualize the estimated gains in productivity, lives saved, and long-term development. For students, the project offers a unique interdisciplinary training ground. Participants will gain hands-on experience in data cleaning and dataset construction, econometric modeling (particularly dynamic panel methods), foundational web development (HTML, CSS, JavaScript), and exposure to AI-assisted development techniques. Beyond the academic setting, the platform holds significant external potential. It could yield publishable results in data science, visualization, or economics journals, and may attract collaboration, sponsorship, or content partnerships with major international organizations such as the FAO or the World Food Programme. Ultimately, the Interactive Data Platfiorm aims to transform scattered statistics into actionable intelligence—empowering researchers, students, and policymakers to ask better questions and design more effective interventions.

Peptide therapeutics are very attractive over small-molecule medications, as they are highly selective, well-tolerated, and have less adverse effects. Generally, the poor oral bioavailability of peptides requires subcutaneous administration. A short half-life poses additional challenges for their formulation and clinical utility. Despite these obstacles, the current rate of approval by the FDA for peptide drugs is twice as fast as for small molecules. Worldwide, 88 peptide drugs are approved, and 170 peptides are currently being evaluated in clinical trials. Peptide stapling, a cyclization technique, is a widely used approach to develop staple peptides. However, the traditional hydrocarbon and triazole/disulfide stapling methods produced low yield and required catalyst separation. Hence, a novel high yielding stapling method is required.   Our long-term goal is to design, synthesize and evaluate the efficacy of novel pi-pi staple and potentially orally active peptide targeting the various proteins related to Neurodegenerative, Infectious and Cancer Diseases. This project has following two specific aims:   Aim 1.  To develop potent staple peptide mimetics: A pool of novel pi-pi staple analogues will be designed and optimized targeting amyloid beta, alpha synuclein in Alzheimer and Parkingson diseases, MDM2 (Double Minute 2 Protein) in cancer and main protease of SARS-CoV-2 and Rhinovirus in infectious diseases, and disease. Computer aided design and solid phase peptide synthesis protocol will be employed.    Aim 2. To evaluate inhibition efficiency, metabolism and stability. To assess the inhibition efficiency, various essays will be conducted. In-vitro metabolic and stability assays will be performed improving half-life, and oral bioavailability.   The expected outcome of this project is to develop the next generation of peptide drug to treat dementia, infectious and cancer diseases and advance our knowledge of how these peptides can be further improved.

Ocean wave is an abundant source of clean energy and is relatively less explored and underutilized compared to other renewables. A novel hybrid wave energy conversion (HWEC) system has been developed that comprises an array of interconnected electromagnetic coil units. These are positioned around a tubular structure which contains a magnetic ball. When the device encounters a wave, a tilting motion results that causes a magnet ball to travel in one direction. As the wave passes and the tilt direction is reversed, the ball begins to travels in the opposite direction. As the ball travels back-and-forth its magnetic field cuts through the stator coils, inducing a voltage and producing electrical energy through electromagnetic induction. The device efficiently harvests electrical energy from oscillations and can be placed on vessel such as small boats, barges or ships that are subjected to wave motion. In order to generate measurable and repeatable data for verifying device output, a testing platform is required to simulate characteristic wave motion. This platform should allow close replication of displacement that is generated when the HWEC system encounters a wave. It is also expected that multiple wave form templates will be developed to closely simulate the displacement of the most common types of wave motion. Currently, an affordable commercially available solution does not exist for this specific testing application.

Digital networks rely on cryptography for security. In this project, students will explore the digital networking protocols, including TLS, SSH, PKI, IPSec, and DNS. Students will investigate the core principles of digital networking and the reliance on cryptography. Students will research the impact of new post-quantum cryptography (PQC) on the security of digital networks.

This research project is looking at the use of AI enabled digital sex work. Existing research on digital sex work has predominantly focused on the use of digital platforms as the medium through which human actors exchange sexual services across space and time. However, research on human-machine relationships, in which at least one actor is human and one or more are virtual, remains in its infancy. While the nature of the relationship is novel, at its root is a political economic reality ground in the logic of capital wherein the digital “pimps” are software engineers, vibe coders, tech-giant advertising firms who profit on the alienation of human intimacy by targeting emotional vulnerability and desire for sociality and sex. The appeal of these relationships are growing at a time of profound loneliness in a hyper-connected world and there is demand across genders and ages. However recent data, suggesting that over half of teen boys rank AI girlfriends above relationships with human women, speaks to a profound shift in intimacy norms that threaten lasting and potentially destructive psychological, sociological, and even biological consequences to our long term wellbeing. Likewise, ethical questions compound when the morality, legality, and psychological consequences of things like child sexual abuse material and deepfake pornography demand social regulation within democratic societies that are guided by the profit seeking motive of capitalism. The goal of this research is to better understand the motivations, reactions, and psychosocial consequences of this emergent form of transactional sexual relationship between a person and a technology company. This research project will consist of 3 main parts: • A thorough literature review of the psychological and sociological research on AI, intimacy, and sex work. • A thematic analysis of news media and films on the topic.  • A qualitative analysis of internet users' comments on related threads and discussion forums.

Development is chaotic -cells are rapidly dividing and need to be sorted. That sorting includes lining cells up in a proper orientation and order. This type of organization, called cell polarity, is important in many tissues -your gut absorbs nutrients and deposits them in your bloodstream. There are disease and developmental consequences to disruption of this order. The Wardwell-Ozgo lab studies the details of how hormones direct development and disease. Hormones are small biomolecular messengers that choreograph development and are often involved in disease development and progression. We have recently generated some exciting data regarding hormone signaling in an organ unique to the juvenile developmental stage of Drosophila, called the salivary glands. When hormone signaling is disrupted, it appears the cells in the salivary gland do not know where they are and are not organized appropriately. The consequences of this confusion is that the organ does not operate correctly. Students joining this project would work with Drosophila crosses to micro dissect the salivary glands, use antibodies and high level microscopy to determine if cells indeed are confused as to where they are.

This documentary explores the story of a researcher developing a medical device for sickle cell disease (SCD). His engineering research and personal passion will be at the center of the story, while the film follows his journey in both the United States and Uganda. The documentary presents him as a warrior fighting an invisible enemy, SCD. Rather than portraying him as a traditional researcher, the film depicts him almost as a secret agent undertaking an extraordinary mission. Along the way, he faces numerous personal challenges, including health issues, uncertainty, and the pressure to succeed. The film will be completed during the academic year, allowing students to gain comprehensive, hands-on experience with the entire documentary production process.

People use AI systems like ChatGPT to learn, work, and make everyday decisions, so it matters that these systems respond fairly and consistently to everyone. However, even when two people mean the same thing, differences in dialect, politeness, or communication style may lead AI systems to respond differently. For example, an AI system may respond differently to “Could you please explain this to me?” and “Explain this to me right now!”, or to the same request expressed in a different dialect, even though the user’s underlying intent has not changed. The way a person phrases a question—including their dialect, level of politeness, or communication style—may influence how an AI system understands and responds to them, even when the underlying meaning is the same. This project will explore an important question: Does the way we speak change how artificial intelligence treats us? Students will investigate how politeness, impoliteness, directness, dialect, and other forms of language variation can affect the behavior of large language models (AI systems). The project builds on existing datasets created using well-known theories of politeness and impoliteness from psychology, communication, and linguistics. Students will learn about these theories, explore how researchers use them to study real communication, and examine how they can be applied to artificial intelligence. A major part of the project will involve examining sentences that have been translated or transformed into different linguistic styles. Students will help determine whether these transformations still preserve the original meaning and whether they accurately reflect the politeness or impoliteness strategy they are intended to represent. Students will independently evaluate examples, compare their judgments, investigate disagreements, and help improve the research guidelines used to evaluate the data. As the project progresses, students will also help examine how different AI models respond to these language variations. They will participate in analyzing results, identifying interesting patterns and failures, and considering what the findings mean for fairness and responsible artificial intelligence. Students will experience multiple stages of the research process, including reading and discussing research, developing research methods, collecting and validating data, analyzing results, and presenting findings. No previous research, programming, or artificial intelligence experience is required. Students from any major who are interested in AI, language, psychology, communication, fairness, or technology and society are encouraged to apply.

Every day, humans and wildlife are exposed to chemicals that enter the environment through plastics, manufacturing, consumer products, and contaminated water and food. Some of these chemicals can interfere with hormones, the chemical signals that guide growth and development. However, we know much less about how exposure to these chemicals early in life affects the developing immune system—the body's defense against disease.This research will examine how two important groups of environmental contaminants affect immune development: bisphenol AF (BPAF), a chemical related to BPA that is used in some plastics and other products, and PFAS, a large group of long-lasting chemicals sometimes called “forever chemicals.” Although these contaminants are increasingly found in the environment, many questions remain about how exposure during early development may influence health later in life. We will study these questions using two very different organisms: chicken embryos and Daphnia, tiny freshwater animals commonly known as water fleas. Chicken embryos provide an excellent model because their development can be studied inside the egg without exposing an adult animal. Students will expose developing embryos to environmentally relevant amounts of BPAF or selected PFAS and examine whether these chemicals change the growth and development of important parts of the immune system. We will also determine whether exposed embryos respond differently when their immune systems are stimulated. Daphnia provide a complementary model with an especially exciting advantage: they reproduce quickly and can be studied across multiple generations. We will expose Daphnia to contaminants during early development and determine whether this changes their ability to respond to later immune challenges. We will then follow their offspring and subsequent generations to determine whether effects continue even after exposure to the contaminant has ended. Together, these studies will address a larger question: Can exposure to environmental contaminants during early development permanently change how the immune system develops and functions? Comparing chickens and Daphnia, two animals separated by hundreds of millions of years of evolution, will also help us determine whether these effects represent fundamental biological responses to environmental contaminants.

Parental involvement is about being emotionally open, reliable, and approachable. Children with emotionally open parents form secure attachments, which leads to higher self-esteem and better peer relationships. Active engagement also boosts language acquisition, cognitive skills, and emotional resilience. Parental emotional and physical presence matters significantly, even during daily routines like bath time, getting dressed, or reading bedtime stories. Each of these interactions reinforces your child’s sense of safety, belonging, and worth. Learning about sensitivity levels is fundamental for communicating with respect. Understanding these levels prevents oversights and avoids harming relationships. Hypothesis: Students who have not gotten enough parental support in their childhood and at the same time have a high sensitivity level, have low self-esteem more often than other students. The prime goal of the project is to prove or disprove the hypothesis by administering a Qualtrics survey to a group of KSU students and analyzing the results. The secondary goal is to get information about an approximate percentage of KSU students who need psychological help.

Humanoid robots are increasingly being developed to operate in natural environments and interact safely and effectively with people and surrounding objects. Although robots can obtain some information through camera and other non-contact sensors, effective physical interaction requires human-like tactile perception—the ability to detect touch and pressure. Human skin provides such capability through specialized sensory nerve endings called mechanoreceptors. Replicating similar functionality in robots remains challenging. The emerging field of flexible and stretchable electronics provide an attractive avenue to realize electronic skins for futuristic robots, particularly by applying nanotechnology to fabricate flexible sensors that promises to exhibit unique electrical, mechanical, and sensing properties that are suitable for humanoid robots. This project focuses on the design, fabrication, and evaluation of flexible electronic touch and pressure sensors by using engineered nanomaterials for humanoid robotic applications. The research will investigate the use of nanomaterials such as graphene and other conductors, semiconductors, dielectric nanoparticles, nanocomposites as active sensing materials. These materials will be incorporated into flexible polymeric or elastomeric substrates to develop lightweight, conformable, and highly sensitive touch sensing devices. The fabricated sensors will be experimentally characterized for touch and pressure sensing performance, including sensitivity, detection range, response and recovery time, repeatability, hysteresis, and stability under repeated mechanical loading. Their flexibility and durability will also be evaluated through repeated bending and deformation tests. These studies will help to identify the most suitable material combinations and device architecture for reliable tactile sensing for robotic applications. To demonstrate practical functionality, fabricated sensors will be integrated onto the fingers of a programmable robotic arm. The system will be evaluated for its ability to detect physical contact and pressure, providing a proof-of-concept demonstration. This approach could ultimately contribute to the development of flexible artificial skin for humanoid robots. Overall, the project aims to innovate novel nanocomposite-based flexible tactile and pressure sensors, while demonstrating their potential to provide human-like touch perception for emerging humanoid robotic applications.  The participating FYSP students will receive hands-on training on the design and fabrication of flexible electronic sensors, electrical and mechanical measurements, and sensor interfacing and integration with electronic circuits and robotic systems. They will gain practical experience in experimental design, materials processing, data acquisition, analysis of sensor performance, and develop many important and useful engineering research skills through this project.

As graduation approaches, many students ask themselves the same question: “Am I actually ready for my career?” The transition from college to the workforce is one of the most important milestones in a student’s educational journey. While earning a degree provides disciplinary knowledge, employers increasingly seek graduates who also possess strong communication, teamwork, problem-solving, professionalism, and adaptability. Previous research conducted by our team examined employer perspectives on workforce readiness and identified a gap between the skills employers consider most important and their perceptions of how well recent graduates are prepared in those areas. We plan to move this project forward in two complementary ways. First, we will build upon our previous quantitative research by conducting focus groups and follow-up interviews with leaders in business, government, and nonprofit organizations. These conversations will provide a deeper understanding of employer expectations and help explain the patterns identified in our previous research. They will also allow us to gather more detailed perspectives on the skills and competencies employers seek in recent graduates.   Second, we will examine workforce readiness from the student perspective. Focusing primarily on undergraduate seniors, the study will explore how prepared students feel to enter the workforce and whether they believe they possess the knowledge, skills, and professional competencies employers value most. The survey will examine students’ confidence in these competencies, their awareness of career development resources, and how experiences such as internships, undergraduate research, campus employment, leadership roles, mentoring, and student organizations relate to their perceptions of career readiness. Together, these two components will provide a more complete understanding of workforce readiness from both the employer and student perspectives. By comparing student perceptions with employer expectations and insights from focus groups and interviews, we can identify areas where student confidence aligns with employer needs and where gaps remain. These findings can help colleges and universities strengthen career readiness initiatives and better prepare students for successful careers after graduation.   This project will also establish a foundation for a longitudinal study. Students who participate in future follow-up studies may be invited to share their experiences after graduation, allowing researchers to examine how perceptions of career readiness change as graduates transition into the workforce and compare those experiences with employer expectations and their perceptions before graduation.

The objective of this project is to understand how mass media portrayals of people with developmental disabilities are perceived by young adults with or without disabilities. Studies have shown that media representations of people with disabilities are not always true to life. Perceived positive media representation of people with disabilities led to affirmation of their disability identity, whereas negative representation led to denial of their disability identity. To the principal investigator’s knowledge, no media scholars have conducted a group discussion consisting of people from both groups (with and without disabilities) to analyze mass media portrayal of people with developmental disabilities. This study aims to understand how the portrayal of people with developmental disabilities (e.g., autism, cognitive disability) is perceived by young adults with disabilities and to shape the understanding of disability by people who do not share the identity of being disabled.

Self-driving vehicles record video sequences that show roads, moving cars, and nearby environments from changing viewpoints. Moving cars often block the road beneath them. Digitally removing a car therefore leaves a blank area in the scene. This project asks whether images captured before and after a car passes can rebuild that hidden road. Students will use open-source self-driving data, prepared code, and computing resources. An existing AI model will identify vehicle regions, so students will not need to train a segmentation model from scratch. The team will then combine observations from different times and viewpoints to reconstruct the stationary road. This 3D reconstruction workflow is based on Gaussian Splatting, a modern method that represents a scene using many small colored 3D elements. Students will create a small benchmark with two types of examples. Controlled examples will place vehicle-shaped masks over road surfaces whose true appearance is known. Real examples will remove moving cars from driving sequences. The team will compare single-image filling, transfer from a nearby video frame, and multi-view 3D reconstruction. Five measures will be used throughout the project: similarity to the known road appearance, visible seams along filled boundaries, coverage (how much of the hidden road the cameras actually saw), consistency across camera views, and computing time. Road areas that never appear in any input view will be labeled as visual guesses rather than presented as true reconstruction. After rebuilding a cleaner road, students will place or reposition provided digital vehicle models to create simple alternative traffic scenes. This activity will demonstrate scene editing rather than traffic-behavior simulation. The main research question will remain the accuracy and limitations of road reconstruction. The project will produce experimental data, reproducible code, visual comparisons, a short demonstration video, and a poster for the Spring Symposium of Student Scholars. No prior experience in artificial intelligence or 3D graphics is required. Students will receive guided tutorials and weekly mentoring.

This project will investigate whether large language models (LLMs) can improve the way individuals identify and access healthcare and community resources. People in rural and underserved communities may face significant barriers to healthcare, including cost, transportation, geographic location, limited availability of providers, and difficulty identifying appropriate resources. At the same time, LLMs are increasingly being used to obtain and interpret health information, creating new opportunities as well as concerns regarding accuracy, reliability, and safety. The study will evaluate two related questions: whether LLM-assisted navigation can identify appropriate healthcare and community resources more efficiently and accurately than traditional resource navigation, and whether requiring LLMs to use verified sources improves the accuracy and safety of health-related responses. Students will develop a structured database of verified healthcare and community resources, including primary care, mental health, maternal health, chronic disease services, transportation, and other community-based resources. They will also evaluate standardized scenarios representing common healthcare-access challenges experienced by rural and underserved populations. These scenarios will be evaluated using three approaches: traditional navigation of a verified resource directory, unrestricted LLM-generated responses, and LLM responses restricted to the verified resource database. The standardized evaluation criteria includes accuracy, completeness, appropriateness, efficiency, source reliability, and the presence of incorrect, unsupported, or potentially harmful information. The study will use publicly available information and standardized scenarios. We will not collect medical records, protected health information, or information from human research participants. By evaluating LLMs as a potential tool for healthcare navigation rather than as a replacement for healthcare professionals, this project will examine both the opportunities and limitations of emerging language technologies in healthcare. Students will gain interdisciplinary research experience at the intersection of LLMs, healthcare, public health, and health equity while contributing to a growing body of research on the responsible and evidence-based use of LLMs in healthcare. This is ideal for students interested in a career in healthcare and/or technology.

Purpose of Study Gambling has expanded far beyond traditional casinos. Today, gambling options extend well beyond licensed casinos to include offshore betting platforms and local underground networks. Instead of choosing just one type of platform, a growing number of players blend legal and illegal gambling. This research project investigates how a player’s choice of gambling platform affects their real-world consequences. Specifically, this study aim to examine how Legal-Only, Illegal-Only, and Mixed (dual-platform) gamblers differ in their socio demographic profiles, gambling addiction severity, gambling harms, and their willingness to seek treatment. Why This Study Matter These three player groups are exposed to fundamentally different gambling environments, ranging from completely unregulated, protectionless spaces to fully regulated systems designed to minimize harm. Consequently, operating within these distinct environments may lead to vastly different levels and types of negative consequences for players. Understanding these differences is essential for designing effective, targeted public health interventions, clinical treatment programs, and regulatory policies to address gambling-related problems.   Research Plan This project involves a secondary statistical analysis of the 2025 National Survey on Gambling Awareness and Status conducted by the South Korean National Gambling Control Commission (n=1032). The research will progress through four structured phases: Phase 1: Dataset Screening & Variable Identification: Review survey codebooks to isolate key variables covering licensed and unlicensed platform usage (online and land-based), Problem Gambling Severity Index (CPGI/PGSI) items, gambling harms, and help-seeking metrics. Phase 2: Group Classification & Recoding: Develop statistical criteria to categorize respondents (N = 1,032) into Legal-Only, Illegal-Only, and Mixed groups based on their past-year gambling participation. Phase 3: Comparative Statistical Analysis: Perform descriptive and multivariate analyses (ANOVA, Chi-square, and regression modeling) to evaluate differences in addiction severity, financial/personal harm levels, and treatment readiness across the three cohorts. Phase 4: Synthesis & Dissemination: Translate empirical findings into academic manuscripts, conference presentations, and policy briefs aimed at enhancing public health and addiction recovery frameworks.

An estimated nearly 50 million people are experiencing human trafficking globally today, an urgent human rights violation that the international and local community has struggled to address. Over the past two decades there has been a rise in the number of anti-trafficking organizations that place survivors of trafficking in leadership roles within the anti-trafficking movement under the premise that survivors themselves are best equipped to respond to this human rights violation. This project is the first to work to track the extent of survivor leadership in anti-trafficking with and evaluate the outcomes associated with survivor leadership. The project will first explore what it means to develop a survivor leadership model in anti-trafficking work by developing a conceptualization and operationalization (definition and measurement) that is cross-culturally relevant. Next, this project will undertake the collection of a data set that will track survivor leadership within anti-trafficking organizations across the US as well as expand to other parts of the international community. This dataset will include quantitative indicators of survivor leadership and enable the research team to explore patterns in survivor leadership and provide descriptive statistics. This research will also collect qualitative evidence through interviews and focus groups with anti-trafficking organization staff.  Broadly speaking, this project will help us answer some of the following questions: What does it mean for an anti-trafficking organization to use a survivor leadership model? Are survivors being valued for their lived experience expertise, or other types of expertise as well? How common is the use of survivor leadership, and how do those rates vary across different cultural contexts? Perhaps most importantly, does the evidence suggest that the presence of survivor leadership is associated with better outcomes for victims and survivors? In other words, does an increase in survivor leadership lead to improvements in how we fight human trafficking?  This project would be of interest to students who are passionate about topics including human rights, labor rights, gender-based violence, nonprofit organizations, and multi-method research because it will feature both quantitative and qualitative research. Ultimately the goal of this research is to better understand the effect of survivor leadership within the anti-trafficking field so that we can continue to improve outcomes for victims and survivors.

This research project explores the potential of wearable exoskeletons to support ear, nose, and throat (ENT) surgeons during physically demanding surgical tasks. ENT procedures often require surgeons to maintain static or awkward postures, including prolonged neck and trunk flexion, elevated arms, and repetitive upper-body movements. These demands can contribute to muscle fatigue, discomfort, and work-related musculoskeletal disorders. The study will evaluate two upper-body exoskeletons—the HAPO Front, which provides anterior trunk support, and the Auxivo OmniSuit, which supports the shoulders and upper limbs. The project aims to assess the usability, comfort, and biomechanical effectiveness of each device during representative ENT surgical activities. Participants will complete tasks with and without exoskeleton support, allowing researchers to examine changes in posture, movement patterns, muscle activity, task performance, and perceived workload. Data collection will use advanced motion sensors, wireless muscle-activity sensors, video recordings, and usability questionnaires. The findings will help determine whether these devices can reduce physical strain without limiting surgeons’ precision, mobility, safety, or ability to perform surgical tasks. First-year student assistants will play an important role in supporting the research process. Their responsibilities will include preparing the laboratory or clinical simulation environment, organizing study materials, and assisting with the setup and calibration of motion sensors, muscle-activity sensors, and video cameras. Students will help fit participants with the HAPO Front and Auxivo OmniSuit, attach sensors, monitor study sessions, record observations, and distribute surveys assessing comfort, usability, freedom of movement, and perceived workload. They may also assist with organizing, cleaning, and entering study data while learning how to follow human-subject research protocols. No previous technical experience is required; students will receive training on all procedures and equipment and will work as part of a collaborative research team led by faculty and experienced student researchers. This experience will provide exposure to human factors, ergonomics, biomechanics, wearable technology, and healthcare research while strengthening skills in teamwork, communication, data management, and professional interaction. Student contributions will support the development of evidence-based recommendations for using wearable exoskeletons to improve surgeon well-being and promote safer, more sustainable surgical practice.

Children’s picturebooks play a crucial role in shaping how young readers understand identity, culture, and social issues. Over the last 25 years, a growing number of picturebooks have depicted Latina/o immigration, reflecting diverse experiences across North and Central America. However, these stories often vary significantly in how accurately and respectfully they represent immigrant communities, language practices, and cultural nuances. This project explores how Latina/o immigration is portrayed in picturebooks published between 2001 and the present. Using Critical Content Analysis , our research team will examine both the text and visual artwork of these books. We will evaluate how stories address topics such as family bonds across borders, bilingualism, legal precarity, cultural heritage, and community strength. Student researchers will curate a database of eligible picturebooks, develop a shared coding system, and analyze selected texts using qualitative software. By examining both written narratives and visual illustrations, the project aims to identify positive counter-narratives that challenge stereotypes, as well as recurring tropes that oversimplify the immigrant experience. The primary goal of this study is to provide educators, librarians, and literacy researchers with insights into selecting culturally authentic literature for K–12 classrooms. Ultimately, this work contributes to building inclusive educational environments for all students.

Cerium oxide nanoparticles (nanoceria) have garnered significant interest in biomedical research due to their unique redox activity, their ability to switch reversibly between Ce³⁺ and Ce⁴⁺ oxidation states. This dynamic behavior enables nanoceria to act as potent antioxidants with demonstrated antibacterial and anti-cancer potential. Separately, bio-glass is a well-established biomaterial used in applications ranging from bone regeneration to soft tissue repair. Our research brings these two promising materials together by incorporating nanoceria into bio-glass matrices. Upon melting, the bio-glass encapsulates cerium in a mixed-valence state, forming a stable glass with embedded nanoparticles. When exposed to aqueous environments, this bio-glass gradually dissolves, releasing ultra-small cerium oxide nanoparticles. The focus of our current investigation is to understand the dissolution behavior of these embedded nanoceria: how they are released, how their size and oxidation states evolve over time, and how their physicochemical properties are influenced by the glass composition and environmental conditions. We have developed an extraction protocol to isolate these nanoparticles from the dissolving glass and are now characterizing their structural and chemical features to assess their suitability for biological applications. We are seeking a motivated student to contribute to this research. The student will help investigate the mechanisms behind nanoceria release and transformation during dissolution and explore the structural dynamics of the nanoparticles using a range of analytical techniques. Goal 1. Fabricate and study glasses with varied concentrations of nanoceria through parametric changes.  Goal 2. Understand the Bioactive glass as it release kinetics of nanoceria from glasses. Using established protocols will be synthesized nanoceria containing glass samples with known Ce3+/Ce4+ ratios using high-temperature furnaces in the glass laboratory. The glass will then be processed to the required particle size using Mixer Mill MM 500-Nano at the glass laboratory for characterization experiments. This will involve several techniques, including Differential Thermal Analysis, Raman Spectroscopy, Fourier Transform Infrared (FTIR) Spectroscopy, and X-ray Diffraction.This project will provide the students with valuable research experience and prepare them for future careers in science and technology. The results of this study will help us explore new medical applications for our nanoceria-embedded bio-glass and support future research.

The concept of respect has been largely overlooked in developmental science. Yet respect or (dis)respect is often central to how adolescents treat people around them. Indeed feeling respected is often connecting to  strong, healthy relationships or whereas feeling disrespected can lead to heartbreaking consequences (e.g. gun violence). Thus, there needs to be a better understanding of adolescent’s conceptualizations of respect/disrespect to better support relationship building and, equally important, how to repair relationships when disrespect has occurred. This work is guided by a developmental model (Harris et al., 2025) which asserts that notions of respect and disrespect are guided by macro-level forces (e.g. racism, sexism) and that respect is a core developmental need of adolescence therefore we must center it in our efforts to create positive youth outcomes. This project will build on the ongoing research on how adolescents (6th-12th graders) think about respect in their school, home, and community. This project uses focus groups to understand adolescents thoughts and feeling. This year, focus group data will be used to build a survey that represents adolescents definitions of respect/disrespect that could be used widely by practitioners  (e.g. educators, social workers) who want to better understand youth thinking about respect/disrespect. Importantly, having a better understanding of what respect looks and feels like during adolescence will support the of youth-centered interventions that build healthy, respectful relationships between adolescents and the people around them.

Medical images such as CT and MRI scans provide detailed three-dimensional views of organs, blood vessels, tumors, and other structures inside the human body. These images contain valuable information for diagnosis, treatment planning, and disease monitoring. However, identifying and outlining important anatomical structures across hundreds of image slices can require substantial time and effort. Artificial intelligence offers a powerful way to automate this process and support faster, more consistent medical image analysis. This project will introduce first-year students to how artificial intelligence is applied to real 3D medical imaging problems. Students will work with publicly available, de-identified medical imaging datasets and explore how AI models can automatically segment anatomical structures from CT scans. The project will follow a complete research workflow. Students will first learn the basic concepts of medical imaging, image segmentation, and artificial intelligence. They will then use Python-based tools and guided research code to prepare data, run established AI segmentation models, and generate predicted 3D segmentation masks. Students will use 3D Slicer to visualize the original medical images, reference annotations, and AI-generated segmentations in both 2D and 3D. This will allow them to visually investigate where an AI model performs well and where segmentation errors occur. Students will also perform quantitative analysis. They will compare AI-generated segmentations with reference annotations using commonly used evaluation measures and will investigate clinically meaningful characteristics such as structure volume, shape, or other measurements appropriate to the selected dataset. Python experience is highly valuable for this project, but it is not required. Students will receive guided Python training and reusable code examples before progressing to the main experiments. The broader goal is to expose students early in their academic careers to the way modern AI research is conducted in healthcare. They will experience the same general research process used by graduate students and professional researchers: understanding a problem, preparing data, applying computational methods, evaluating results, identifying limitations, and communicating scientific findings. My previous First-Year Scholars successfully progressed from introductory AI research to presenting their work at university, regional, and national research venues. This project will build on that mentoring experience while introducing a new group of students to the rapidly growing field of AI-driven 3D medical image analysis.

People living with dementia may have changes in mood, comfort, attention, and daily activity that are not always easy for caregivers to notice right away. This research project explores how information from simple wearable devices, such as wristbands, may help us better understand these changes and support more personalized care. Students will work with an existing collection of anonymous information gathered during music-based care sessions. The information includes heart-rate patterns, skin temperature, movement, and notes about how a person appeared to feel or take part in an activity. Together, we will explore whether these different kinds of information can help identify times when a person seems calm, engaged, upset, or less involved.   This project welcomes students from any major. Students interested in health, psychology, aging, family care, music, data, design, communication, ethics, or problem-solving can make valuable contributions. No previous experience with programming, research, or wearable devices is required. Curiosity, care for others, and a willingness to learn are enough to begin. Students will receive step-by-step guidance and can take on roles that fit their interests and strengths. Possible activities include reading about dementia care, organizing information, checking data quality, creating simple charts, comparing patterns, helping develop and test computer-based tools, documenting our work, and explaining results for different audiences. Students will meet regularly with the research mentor and team, share progress, and learn how to ask thoughtful questions about real-world problems.   By the end of the year, students will better understand how research can connect health, technology, and compassionate care. They will gain practical skills in teamwork, careful analysis, clear writing, presentations, and responsible handling of health-related information. Students will also prepare a poster or presentation for the Symposium of Student Scholars. This is an opportunity to contribute to a project that may help caregivers notice important changes sooner and better support people living with dementia.

Fluid flow is everywhere—from the water flowing through household plumbing and drinking water treatment plants to the air moving around vehicles and buildings. Engineers must understand how fluids behave to design safe, efficient, and sustainable infrastructure. However, because fluid motion is often invisible and difficult to measure, engineers increasingly rely on Computational Fluid Dynamics (CFD) to visualize and predict fluid behavior. This project introduces first-year students to the fundamentals of CFD through a hands-on, faculty-mentored research experience. Using user-friendly engineering software, students will learn how computer simulations can be used to investigate simple fluid flow problems and answer engineering questions. Rather than focusing on advanced mathematics or programming, the project emphasizes conceptual understanding, scientific inquiry, and problem-solving. Students will select a simple engineering system—such as flow through a straight pipe, a pipe bend, a nozzle, or flow around a cylinder—and investigate how geometry and operating conditions influence flow patterns, pressure loss, and velocity distribution. They will build computational models, perform simulations, visualize results using colorful contours and streamlines, and compare their findings with engineering equations and published reference data. Through this process, students will gain an appreciation for both the capabilities and limitations of computational modeling. By participating in this project, students will: • Learn the fundamental principles of fluid flow and Computational Fluid Dynamics (CFD). • Gain hands-on experience using engineering simulation software. • Develop skills in scientific reasoning, data analysis, and technical communication. • Compare computational predictions with engineering theory and discuss sources of uncertainty. • Experience authentic undergraduate research by investigating an engineering question and communicating their findings through a report and presentation. No prior experience in fluid mechanics, programming, or CFD is required. The project is designed specifically for first-year students and emphasizes exploration, creativity, and learning by discovery. By the end of the project, students will have completed their first engineering research study while developing computational skills that are increasingly valuable in civil, environmental, mechanical, aerospace, and biomedical engineering.

Cellular wireless communication has advanced significantly over the last four decades, evolving from basic voice services in the first generation (1G) to real-time applications and ultra-fast internet connectivity in fifth-generation (5G) networks. Building directly on these foundations, the ongoing transition toward sixth-generation (6G) systems promises to push the boundaries of wireless connectivity even further, laying the groundwork for truly intelligent, hyper-connected environments. Today, 5G serves as a major breakthrough in wireless communication, offering ultra-reliable, ultra-low-latency, and massive machine-to-machine communication. As the backbone of emerging technologies such as the Internet of Things (IoT), autonomous vehicles, and smart cities, 5G and the upcoming 6G play a leading role in shaping the future of global connectivity. To handle escalating demands for speed, reliability, and security, modern cellular networks and upcoming 6G architectures rely on innovative wireless access schemes, such as massive multi-antenna systems, reconfigurable intelligent surfaces (RIS), and non-orthogonal multiple access (NOMA). Additionally, Artificial Intelligence (AI) plays a very important role in enhancing network performance and efficiency. Adopting AI in wireless resource management and optimization allows for faster, highly dynamic networks, which represent a critical requirement for next-generation cellular infrastructures. With that said, understanding these advanced systems is a cornerstone of training the next generation of engineers and researchers. This project aims to establish an undergraduate research team in which students will be actively engaged in designing, testing, and simulating modern 5G and 6G modules and systems. Additionally, participants will investigate various key performance metrics, technical trade-offs, and system bottlenecks, while collaborating to brainstorm creative solutions to these issues. Designed specifically for first-year students, the project provides a strong technical foundation in wireless communication concepts alongside hands-on exposure to practical system models and simulations. The goal of this project is to promote early engagement in research and development by combining strong theoretical knowledge of network architecture with practical modeling techniques. As the project progresses, students will learn and apply simulation tools like MATLAB and its specialized toolboxes to model different 5G and 6G scenarios. Furthermore, they will help simulate advanced communication setups featuring sophisticated resource allocation schemes, and compare their simulation results directly to current state-of-the-art standards.

This project builds on ongoing research investigating whether small vibrations caused by traffic, wind, and other everyday sources can be used to identify changes in bridges. One challenge is that these ambient vibrations are not always strong or consistent enough to measure clearly. A device that can apply controlled vibrations to a bridge could make these measurements more reliable Students will design and build a small vibration-generating device using an electric motor and an adjustable rotating mass. Changing the motor speed, the amount of rotating mass, and its distance from the motor shaft will change the frequency and strength of the generated force. Students will develop the design, select the components, and assemble a safe working prototype. The prototype will be tested on small steel bridges previously constructed by KSU’s ASCE Steel Bridge teams. Sensors placed on the bridges will record their response at different motor speeds and device configurations. The project team will evaluate whether the device can produce repeatable vibrations and whether the measured data can be used to identify the natural frequency of each bridge. If successful, the prototype will provide a starting point for developing a larger portable device for future field testing on bridges.

Micro- and nanoplastics (MNPs) are widespread contaminants that pose growing environmental and public-health concerns. Although membrane filtration has strong potential for removing these particles from water and wastewater, conventional polymeric membranes often face limitations such as high operating costs, fouling, incomplete rejection, and limited ability to separate MNPs by size for recovery and reuse. This project will investigate functionalized membranes for the selective removal and fractionation of MNPs from water. The study will examine the physical and chemical interactions between MNPs and membrane surfaces, including deposition, adsorption, electrostatic interactions, and size exclusion. It will also evaluate how membrane pore size and surface charge influence particle transport and separation. Microfiltration, ultrafiltration, and nanofiltration membranes will be modified using graphene oxide and selected polyelectrolytes, including polyacrylic acid and polyallylamine hydrochloride. The central hypothesis is that these modifications will improve MNPs rejection and enable selective fractionation through combined charge- and size-based mechanisms. The proposed membrane platform is expected to improve separation efficiency, reduce the energy required to treat mixed particle sizes, and support the recovery and potential reuse of fractionated plastic particles.

Glucose levels naturally rise and fall throughout the day, especially after meals. These changes are usually measured using blood tests or continuous glucose monitors. However, emerging wearable technologies—such as smartwatches, fitness trackers, and motion sensors—can also collect information about how people move. This project will explore whether small changes in a person’s walking pattern may be associated with glucose peaks after eating. First-year scholars will work with data collected from wearable motion sensors and continuous glucose monitors. Students will learn how to organize and synchronize information about meals, glucose levels, and walking activity. They will help identify post-meal glucose peaks and examine simple walking characteristics, including step rate, movement intensity, walking consistency, and variation between steps. Students will then create graphs and use introductory data-analysis techniques to investigate whether walking patterns differ when glucose is stable, rising, near a peak, or falling. This is an exploratory research project. It is not intended to diagnose diabetes or replace existing glucose-monitoring devices. Instead, the goal is to determine whether walking data could provide useful complementary information for future wearable health-monitoring systems. Students will gain hands-on experience in wearable technology, digital health, human-centered research, data visualization, and introductory artificial intelligence. They will also learn about research ethics, participant privacy, teamwork, and communicating scientific findings to different audiences. Depending on the project’s progress, students may contribute to a research poster, presentation, demonstration, or publication. No previous experience with glucose monitoring, wearable sensors, programming, or artificial intelligence is required. We welcome curious and motivated students from computing, engineering, health sciences, biology, exercise science, and related fields. Training and mentoring will be provided throughout the project.

Large language models (LLMs) are rapidly changing how people write software, but can they do more than simply generate code? This project will explore whether an AI agent can act as an intelligent assistant for high-performance computing (HPC)-based graph analytics, where powerful computers use many processors simultaneously to solve complex graph problems. Graphs are mathematical structures used to represent connected systems such as social networks, transportation networks, communication systems, and the Internet. An important challenge is making graph algorithms run efficiently on modern multicore processors and computing clusters. Students will help develop an LLM-based AI agent that can take a graph problem described by a user, understand the computational task, and assist with creating and running parallel programs. Unlike a traditional chatbot, the agent will interact with computing tools. For example, it may generate code, prepare an experiment, compile and execute a program on computing resources, examine errors or performance results, and suggest improvements. The project will also investigate retrieval-augmented generation (RAG), which allows the AI agent to retrieve relevant technical information before making decisions. Students will experiment with well-known graph problems such as finding shortest paths, exploring networks, and ranking important vertices. They will investigate questions such as: Can an AI agent  pre-process a graph for an existing graph analysis tool? Can an AI agent correctly parallelize graph algorithms? Can it learn from compilation errors and performance results? Does providing relevant information through RAG improve its decisions? Can an AI agent determine how computing resources should be used for a particular problem? No prior experience with artificial intelligence, parallel computing, or graph algorithms is required. Students will learn these concepts progressively while contributing to the design, implementation, and evaluation of an emerging approach that combines AI agents with HPC.

Want real marketing experience before you graduate? Join a student consulting team working with Vida Volunteer, an international nonprofit that connects healthcare volunteers with communities around the world. Vida serves approximately 800 volunteers each year and wants to expand its reach through stronger marketing and recruitment strategies. You will work on a real client project, apply concepts from your marketing courses, and develop recommendations that the organization can implement. What You Will Do • Analyze how potential volunteers discover and engage with Vida. • Evaluate digital marketing efforts, including social media, SEO, Google Ads, email marketing, and AI search visibility. • Research competing organizations and identify ways Vida can stand out. • Identify opportunities to improve the volunteer recruitment process. • Help develop a 12-month marketing growth strategy with clear recommendations and success metrics. • Present your findings to the organization's leadership. What You Will Gain • Real consulting experience with an international nonprofit. • Hands-on experience in digital marketing and marketing strategy. • Skills in market research, competitive analysis, and consumer behavior. • Experience working with a client and solving a real business problem. • A strong project to showcase on your resume and LinkedIn profile. • Professional references based on your performance. Who Should Apply? This opportunity is open to first-year undergraduate students who are interested in marketing, business, digital marketing, analytics, consulting, or nonprofit organizations. No prior consulting experience is required. The most important qualities are curiosity, reliability, professionalism, and a willingness to learn. Team Size Only 5 students will be selected. Expected Commitment Approximately 5 to 10 hours per week throughout the semester, including weekly team meetings. Why This Project Matters Every additional volunteer recruited through better marketing helps expand access to medical, dental, and veterinary care in underserved communities. Your work will help a nonprofit increase its impact while giving you valuable professional experience early in your college career.

Do you want to conduct real medical research? Do you want to learn more about analyzing data and using those results to help babies? Do you like working in teams? Do you work hard, but want to have fun while you're working? Then, joining the Humans Studies Lab at KSU may be a good fit for you! Each year approximately 10% of babies are born before their due date, referred to as prematurity or preterm birth. The earlier a baby is born, the more medically fragile they are. Being born early puts these infants at risk of dying or developing potentially long-term conditions like chronic lung disease and developmental delays. Many of these infants require specialized care which they receive within the neonatal intensive care unit (NICU). After infants become medically stable, care within the NICU turns to helping the infants grow. An infant’s size at birth and growth over time are important indicators of an infant’s nutritional needs, as well as their risk for developing long-term conditions. Providing doctors with more information about growth, risk and long-term outcomes related to infants’ size is important for clinical practice and long-term health and development for the infant. The Human Studies Lab at KSU conducts research with the goal of saving infant lives and improving the quality of lives saved. We use a large, real medical data set and applied statistics to answer important clinical questions related to preterm infant growth and outcomes. We conduct descriptive and predictive studies, as well as create applied tools, to help doctors and clinicians understand more about infant grow. Recent studies address questions like which infants are most at risk of dying or developing long-term outcomes at birth and during their stay in the NICU and describing how a mother’s health impacts an infant’s size. The lab consists of KSU faculty and students (both undergraduate and graduate) that work collaboratively with infant doctors, dieticians, epidemiologists, and researchers from other institutions. We work hard but have fun working! Students who participate in lab get unique hands-on experience using real, large data and applied statistical methods in a collaborative environment. Join us in the exciting journey of helping babies grow!

Artificial Intelligence (AI) is now a critical technology for every sector of society, e.g., remote sensing, healthcare diagnostics, financial risk assessment, security and surveillance, autonomous vehicles, cybersecurity, and intelligent transportation. Large Language Models (LLMs) and Vision-Language Models (VLMs) have become the backbone of many of these applications, powering everything from conversational assistants to multimodal decision-support systems. Achieving robust, superior performance is a challenge where these models need to address out-of-distribution data and test-time adaptation. Despite this state-of-the-art performance, LLMs and VLMs remain largely black-box systems: their internal reasoning is difficult to interpret, their behavior can be manipulated through carefully crafted inputs, and their training pipelines create new attack surfaces that did not exist in earlier, narrower AI systems. This opacity is not merely an academic concern  it directly undermines the trust required to deploy these models in safety-critical systems. A malicious actor can deploy attacks, e.g., evasion, data poisoning, model extraction, prompt injection, and jailbreaking, exploiting the weakness of this model.  This research designs and develops a trustworthy framework that integrates adversarial robustness, out-of-domain adaptation, and explainability techniques into the core evaluation and mitigation pipeline.

Elections are central to American democracy, yet many voters are unsure how election administration works. Who is responsible for voter registration, operating polling places, counting ballots, certifying results, and resolving disputes? Why do election rules and procedures vary across states and localities? How do people decide which sources of election information to trust? This project invites first-year students to help study how voters understand election administration and how clear, credible information can shape knowledge, trust, and attitudes about elections. In the United States, election responsibilities are divided among local, state, and federal institutions. When voters misunderstand those responsibilities, they may also misidentify where accountability lies and which information sources are most relevant. The project builds on ongoing research examining what Americans know—and misunderstand—about who administers elections. Students will help analyze survey and experimental data on public attitudes and examine voter information. Depending on the stage of the project, they may study how people respond to explanations of election procedures, the roles of election officials, and institutional responsibility. Students may also examine how voters respond to unfamiliar voting rules, such as ranked-choice voting, as an extension of the project's broader focus on how people learn about elections. This part of the project will ask how clear explanations and trusted sources shape people's evaluations of election procedures and proposed changes to voting rules. First-Year Scholars will work as members of a small research team. Depending on student interest and experience, they may review background research, organize and interpret survey data, code open-ended responses or official voter information materials, identify patterns in public attitudes, create basic tables or figures, and prepare findings for the Symposium of Student Scholars. No prior research experience is required, and students from any major are welcome. The project may be especially appealing to students interested in elections, democracy, public opinion, civic education, communication, data analysis, or electoral institutions. By the end of the project, students will have contributed to original research on election administration and public trust. They will leave with practical research skills and a deeper understanding of how voters make sense of the rules, institutions, and information that shape American elections.

Every cell in the body contains the same DNA, yet some cells become muscles, others become neurons, and others become skin or reproductive cells. How cells maintain these identities throughout life is one of the fundamental questions in biology. In this project, students will help investigate how cells "remember" their identity using a tiny, harmless roundworm called Caenorhabditis elegans (C. elegans). Although these worms are only about one millimeter long, they share many important biological processes with humans, making them an excellent model for studying development, genetics, and disease. Students will learn how scientists use genetics and microscopy to understand how genes are turned on and off during development. Specifically, we will examine genes that help control which cellular programs remain active and which are silenced. When these regulatory systems fail, cells can lose their proper identity, leading to developmental problems and disease. Participants will work directly with living organisms, collect and analyze data, capture microscope images, maintain worm populations, and communicate their findings to others. No prior research experience is required. Students will receive hands-on training and mentorship while becoming part of an active research laboratory. This project is ideal for students interested in biology, medicine, genetics, biotechnology, or scientific discovery. Participants will gain valuable research experience while contributing to ongoing studies funded by national research agencies. At the end of the program, students will present their results at KSU's Symposium of Student Scholars and learn how scientific discoveries are communicated to the broader community.

Sexual harassment and sexual assault are important social issues that affect individuals, workplaces, schools, and communities. However, people do not always agree about what behaviors constitute sexual harassment or sexual assault. The same situation may be interpreted differently depending on the information available, the relationship between the people involved, social expectations, or beliefs about gender and sexual behavior. This research project will examine how people make judgments about situations involving sexual harassment and sexual assault. Students will work with the Social Perception and Intergroup Relations (SPAIR) Lab to investigate questions such as: What factors influence whether people identify a behavior as sexual harassment or sexual assault? How do people evaluate individuals involved in these situations? How do stereotypes and social expectations influence perceptions of victims and perpetrators? Why might different people interpret the same situation differently? Students will contribute to an original research project examining these questions. During the project, they will learn how psychological researchers study sensitive social issues and how research can help us better understand attitudes, beliefs, and decision-making. Students will participate in activities such as reviewing previous research, helping develop research materials, testing study procedures, organizing data, and communicating research findings. No previous research experience is required. The project is designed to provide first-year students with an introduction to the research process while allowing them to contribute meaningfully to an active research study. Students will work closely with the PI and other members of the SPAIR Lab and will receive training throughout the project. More specifics about Dr. Kulibert of the SPAIR lab can be found at danicajk.com. By the end of the program, students will have contributed to research examining an important social issue and will present their findings at Kennesaw State University's Spring Symposium of Student Scholars. Through this experience, students will learn how researchers ask questions, collect evidence, interpret findings, and communicate research to others. This project would be great for psychology students who are thinking about a career in social psychology, I/O psychology or human resources, business or marketing, non-profit work, conflict management, clinical/couseling psychology or social work, and victim advocacy. This project is also great for students interested in non-psychology majors including sociology, gender studies, political science, international affiars, business administration, criminal justice, human services, intergrative health science, interdisciplinary studies, organizational and professional communication, public health, data science, and public relations.

Languages change as communities grow, migrate, become bilingual, or shift toward a more widely used language. Mathematical models can help explain these changes, but model conclusions depend heavily on the quality and age of the data used. This project will update a previously published study of language competition by collecting recent census and survey data for several multilingual communities, including Welsh and English, Gaelic and English, Basque and Spanish, Catalan and Spanish, French and English in Canada, and Spanish and English in the Houston area. First-year scholars will work as a research team to locate reliable public data, record how each source defines language ability and language use, organize the information into a consistent dataset, and evaluate whether measurements from different years can be compared fairly. This is important because ''speaks a language,'' ''uses a language at home,'' and ''uses two languages regularly'' describe different behaviors. After the dataset is prepared, students will use accessible computational tools to reproduce and test mathematical models from the earlier study. They will compare model predictions with the newly collected observations, identify cases in which the models perform well or poorly, and explore whether newer models provide better explanations. Students will create graphs, summary tables, and written interpretations of their findings. The project is designed for students from any major who are interested in languages, culture, data, mathematics, computing, or social change. No previous research or programming experience is required. Training will be provided in data documentation, spreadsheet organization, introductory Python, visualization, model evaluation, responsible research practices, and scientific communication. The work will produce an updated research dataset, reproducible analyses, a symposium presentation, and material that may contribute to a student-coauthored scholarly publication.

Plant diseases caused by bacterial pathogens threaten crop productivity and food security worldwide. One important pathogen is Xanthomonas vasicola, a bacterium that causes leaf streak disease in economically important crops. Despite its agricultural significance, we still do not fully understand how this pathogen spreads through plant tissues, which cells it targets, or how plants respond to infection. This project will use glowing biological markers to visualize both bacterial spread and plant responses during disease development. Students will modify bacteria to produce light signals using genes derived from naturally light-producing organisms, such as green fluorescent protein from jellyfish and luciferase from fireflies. These glowing bacteria will then be used to infect plants, allowing students to track infection within living plant tissues over time and at different levels of detail. Using bioluminescently labeled bacteria, students will first visualize entire leaves and whole plants to monitor the progression of infection throughout disease development. Next, students will investigate how communication between neighboring plant cells influences bacterial spread and disease progression. This will be accomplished by chemically restricting communication between cells and using bioluminescence imaging to visualize and quantify the spread of infection across whole leaves over time. Next, using advanced fluorescence microscopy, students will examine tissues infected with fluorescently labeled bacteria at higher resolution to determine where bacteria accumulate and identify the plant cell types most affected during infection. Finally, to understand how individual plant cells respond to infection at the subcellular level, students will investigate proteins used by bacteria to alter the behavior of plant cells during disease. These bacterial proteins will be fluorescently labeled, expressed in plant tissues, and visualized using fluorescence microscopy to determine where they accumulate inside cells and which cellular activities they influence during infection. The project integrates molecular biology, microbiology, plant biology, and advanced imaging to address fundamental questions in plant disease and immunity. Students will participate in creating engineered biological materials, conducting plant infection experiments, collecting imaging data, quantifying biological responses, and analyzing results. No prior research experience is required. As active contributors to an ongoing research program, students will generate original data that address unanswered questions in plant disease biology and contribute to the broader scientific understanding of how plants and pathogens interact. By the end of the program, participants will have produced datasets suitable for presentation at the Symposium of Student Scholars and that may contribute to future scientific publications and externally funded research projects.

“Cognitive control” refers to a broad set of mechanisms that humans (and perhaps other organisms) use to ensure their behavior and mental processes align with their goals. Cognitive control is thought to be broadly important for a host of psychological processes from self-regulation to sensory discrimination. However, individual differences in cognitive control, that is, differences across people in their ability to voluntarily direct their cognition, are poorly understood. This project will investigate the possibility that many psychological investigations might underestimate cognitive control by ignoring proactive control, in which people actively prepare for upcoming events, in favor of reactive control, in which individuals engage in control processes only after an event occurs.

Background Do you want to help build the batteries of the future—batteries that charge faster, last longer, and are much safer? As our reliance on lithium-ion batteries continues to grow, we need energy-storage technologies that are more powerful, durable, and reliable. However, the current battery technology still faces important safety and performance challenges. Conventional lithium-ion batteries use a flammable liquid electrolyte, which can contribute to fires under extreme conditions such as physical damage, high temperatures, or internal short circuits. An even more challenging problem can happen inside the battery, where it is difficult to see or detect. During repeated charging and discharging, unwanted reactions can occur at the interfaces between the battery materials. These reactions can cause tiny, needle-like structures called dendrites to grow inside the battery. Over time, dendrites can damage the battery and eventually create an internal short circuit—even when the battery appears to be operating normally. Method and objective  Our goal is to build a “smart battery” that lets us see what happens inside a battery while it operates. By monitoring these changes in real time, we aim to understand how batteries lose performance and how early signs of failure develop. These insights will connect battery design, internal changes, and performance, helping guide the development of safer, more reliable, and longer-lasting next-generation solid-state batteries.   Students will participate in several stages of the research: • Make new battery materials: We will synthesize and characterize a novel transparent solid electrolyte developed by our research team. • Build smart batteries: Students will help design and fabricate specialized smart battery cells using the newly developed transparent electrolyte • Watch the battery in action: We will use optical microscopy, Atomic Force Microscopy (AFM), electrochemical measurements, and FTIR spectroscopy to monitor how the battery changes during charging and discharging. • COMSOL modeling    No prior battery research experience is required. You will receive hands-on training and learn laboratory, characterization, data analysis, and computational skills. As you gain experience, you will have opportunities to explore your own research questions and take ownership of your work.   Where Can This Research Take You? You will gain hands-on research experience, learn to use advanced tools, and work alongside graduate students and faculty. Your work may also lead to research publications, patents, and conference presentations, giving you valuable experience as a young researcher.

Natural disasters and emergency situations often place people and first responders in dangerous environments where rapid access, assessment, and delivery of aid are difficult. Flooded communities, collapsed buildings, wildfire zones, and damaged infrastructure can delay rescue efforts and expose both victims and emergency personnel to significant risk. While conventional drones have become valuable tools for aerial imaging and surveillance, their rigid structures limit their ability to safely interact with people and objects in complex environments. This project seeks to create a new class of soft robotic drones that combine the mobility of aerial vehicles with the safety, adaptability, and resilience of soft materials. Inspired by birds and natural organisms, these drones will incorporate flexible robotic arms, compliant grippers, and impact-tolerant structures that allow them to operate safely in close proximity to people and infrastructure. Unlike conventional drones that primarily observe from a distance, soft robotic drones will be capable of physical interaction. They will be designed to deliver emergency supplies, retrieve critical items such as medications and communication devices, open or manipulate lightweight obstacles, and assist search-and-rescue teams in locations that may be inaccessible or hazardous for humans. The soft components will help reduce the risk of injury during accidental contact and enable safe operation in cluttered environments where collisions are difficult to avoid. Beyond immediate emergency response, the technology could support humanitarian missions by providing rapid assistance to underserved or disaster-stricken communities. Potential applications include post-disaster damage assessment, delivery of food and medical supplies, infrastructure inspection, and support for public safety operations. The resulting systems could improve response times, enhance situational awareness, and help save lives while reducing risks to emergency personnel. The project will also create educational and workforce-development opportunities by engaging undergraduate and graduate students in interdisciplinary research spanning aerospace engineering, robotics, artificial intelligence, advanced manufacturing, and materials science. Outreach activities will introduce K-12 students to emerging technologies and help develop the next generation of engineers and innovators. By advancing safe and intelligent aerial robotics, this project aims to transform how communities prepare for, respond to, and recover from emergencies. The long-term vision is to establish a new generation of human-centered robotic systems that make disaster response faster, safer, and more effective while strengthening regional innovation and economic growth in advanced robotics and aerospace technologies.

The U.S. undergraduate population contains a growing number of post-traditional students with responsibilities such as financial commitments, care for families, and military service in addition to enrollment in undergraduate programs (Bloomberg, 2023). In colleges of education, preservice teachers are balancing the time and financial demands of unpaid student teaching with their necessary paid jobs and other social responsibilities, which can contribute to increased stress and burnout (Andzik et al., 2023; Camacho et al., 2021). These factors, coupled with the requirements of teacher preparation programming, have significantly impacted the teaching profession and the experiences of preservice teachers (Lindqvist et al., 2020).   Traditional models of teacher preparation may implement inflexible structures, such as unchangeable program sequences, limited instructional modalities, and non-negotiable approaches to clinical internship, paired with high-stakes evaluations to address state accreditation policies. Pre-service middle grades teachers (grades 4-8) are tasked with developing both a depth of interdisciplinary knowledge and pedagogical practices to address the nuanced social and cognitive needs of young adolescents (Bishop & Harrison, 2021), alongside the other demands and rigors of education preparation programming. Thus, an exploration of factors in middle grades preservice teacher burnout in the contemporary educational context is a timely research inquiry.  The aims of this study are to understand factors in preservice and early career teacher burnout and to consider strategies for sustainability. These aims will be achieved through an interrogation of the experiences and perspectives of preservice teacher candidates and recent program graduates from the B.S.Ed. in Middle Grades Education program within the Bagwell College of Education, using the following questions to guide this project:  • What factors contribute to middle grades preservice teacher burnout in the current educational climate?  • What strategies help to mitigate middle grades preservice teacher burnout and facilitate sustainability?  • What are the implications for programs of middle grades education?  Qualitative methods will be applied, and data collection tools will consist of a survey questionnaire and semi-structured focus groups. The results of this study will add perspectives to trends and issues in middle grades education research and potentially inform policy for sustainability and engagement in contemporary models of middle grades teacher preparation programming.

AI recommender systems are now widely used across retail settings to facilitate consumer decision-making. These systems use information about consumers’ preferences and behavior to determine what to recommend. Resulting recommendations differ in how closely they follow what the system has learnt about a consumer. Some closely reflect learned consumer preferences, while others deviate from those preferences while still presenting potentially relevant options. This project asks: How closely should AI recommendations align with consumer preferences to best support decision quality? Recommendations that closely reflect existing preferences may make decisions easier, while recommendations that move somewhat beyond those preferences may help consumers discover options they would not otherwise consider. The research will examine how these different levels of alignment affect consumers’ sense of autonomy over their choices and consequently their decision quality (efficiency, satisfaction and confidence), with implications for overall consumer wellbeing in their digital/omnichannel interactions with companies. It will also investigate for what type of online environments (streaming, social media, or retail) and for what consumer goal (entertainment or shopping) closer versus looser preference alignment produces optimal decision quality.

This project will develop a rigorous mathematical theory for laser-based power beaming: the controlled transmission of optical energy over long distances to remote receivers. Power-beaming systems must maintain beam intensity and stability while accounting for diffraction, atmospheric dispersion, absorption, turbulence, nonlinear optical effects, and interactions between the beam and the atmosphere. Existing laser-propagation models provide a hierarchy ranging from Maxwell’s equations to envelope-based equations, but many of these models rely on approximations whose mathematical validity and reliability are not fully understood. The resulting theory will provide a mathematically justified foundation for designing robust power-beaming systems for remote sensing platforms, high-altitude vehicles, satellites, and infrastructure in difficult to access environments. More concretely, the research will establish an analytical framework connecting Maxwell equations with computationally tractable propagation equations. Both analysis and numerics will be involved in the project.

Sleep is something we all experience, but we usually do not know what is happening to our breathing pattern while we are asleep. During a normal night, breathing changes naturally as we move through different stages of sleep. In some people, however, the airway can become partially or completely blocked, causing repeated interruptions in breathing. These events can disturb sleep and cause a disease called sleep apnea. Diagnosing sleep apnea often requires an overnight sleep study in which over 20 sensors are attached to the body to monitor breathing, oxygen levels, and heart activity. Unfortunately, this process can be inconvenient and uncomfortable. Researchers are therefore exploring simpler technologies that could help monitor sleep at home. One promising source of information is the sound of our breathing. This project asks a simple but exciting question: Can artificial intelligence (AI) learn to understand what is happening in the body just by listening to breathing sounds during sleep? We will work with real sleep and breathing recordings collected in research studies. These recordings include breathing sounds along with signals showing how air moves in and out during breathing, how hard the chest and abdomen are working to breathe, blood oxygen levels, and heart rate. We will explore how these different signals relate to one another and what information may be captured in sound alone. For example, we will examine whether breathing sounds change when someone has trouble breathing, whether the sounds change before oxygen levels drop, and whether breathing sounds are different during different stages of sleep. Our ultimate goal is to develop an AI model that can learn meaningful information about a person’s sleep health from the breathing sounds.  First-year scholars will contribute to this exciting research project. No previous experience in sleep research, artificial intelligence, or programming is required. Students will learn the necessary Python, data analysis, signal-processing, and machine-learning skills while working with the research team. By the end of the project, students will have experience working with real biomedical data, developing research questions, creating scientific figures, interpreting results, and presenting their findings at the KSU Symposium of Student Scholars.

Short Summary: This project aims to address the complexity of music notation for learners with disabilities in music therapy settings. You can be part of a research team that collects and analyzes data gathered at a music therapy clinic (located ~16 miles away from the Kennesaw campus). Music therapy clients will use a music notation technology designed and developed at KSU (called SpectrumPlay) to support musical play. We will be examining how the tool supports user motivation and individual therapy goals.  We warmly invite first-year scholars from a diverse range of fields to assist with field research by conducting observations and gathering data in a music therapy clinic setting. Backend analytics will be captured through Google Analytics 4, and the technology will be further developed based on iterative user feedback. Meetings with the research team would be bi-weekly online or at KSU’s HatchBridge Incubator. Data analysis and conference proposal/presentation/manuscript opportunities would also be built into the project.  Full Project Description: This project addresses an existing problem in music education, therapy, and treatment support settings for individuals with developmental disabilities. It is currently difficult to quickly and efficiently create adapted music instruction for those with neurodevelopmental disorders (NDDs). One effective intervention for enhancing the cognitive and behavioral development of individuals with NDDs is music therapy. For caretakers, educators, facilitators, and therapists who guide NDD learners in musical play, there is no quick or efficient way to create personalized, adapted music instruction or therapy for learners with developmental disabilities, yet the need is great.  To address this issue, Dr. Roman, an Associate Professor in the School of Instructional Technology and Innovation (SITI), and elementary music teacher and SITI doctoral candidate, Erin Collins, co-designed a digital tool that personalizes inclusive music learning. The tool they created, SpectrumPlay, simplifies music notation into scaffolded levels. Intended to be used with a color-coded melodic instrument, SpectrumPlay meets learners where they are, supporting independent musical play and success by eliminating the complexities and barriers of music notation. This project is an excellent opportunity for students interested in field research of any kind. If you have ever learned to play an instrument and/or have an interest in business, psychology, social sciences, music therapy, elementary or inclusive education, learning technology design, User Interface (UI) and User Experience (UX) research, computer science, and/or data science and analytics, this project is for you!

Graduate school can be an exciting and transformative experience, but what happens when grief and loss interrupt the journey? Graduate students may experience the death of a loved one, caregiving transitions, relationship changes, family crises, or other significant losses while simultaneously trying to complete coursework, conduct research, meet academic milestones, and progress toward graduation. These experiences can affect students' well-being, academic engagement, sense of belonging, and ability to persist in their programs. Mapping a Grief-Conscious Ecosystem for Graduate Student Success explores how colleges and universities can better support graduate students navigating grief and significant loss. The project will begin by examining the experiences of doctoral students within Kennesaw State University. Through qualitative interviews, the study will explore how grief and loss have affected students' educational experiences, academic progression, and decisions about continuing their graduate education. The research will also examine the types of support students received, the resources they knew about or used, and the barriers or gaps they encountered when seeking support. The project will then map the broader ecosystem surrounding graduate student success, including the people, programs, policies, services, and resources students may encounter when navigating a significant loss. Particular attention will be given to how students move through these systems, where responsibility for providing support currently resides, how students are connected to available resources, and where support may become fragmented or difficult to navigate. By comparing existing institutional resources with graduate students' lived experiences and identified needs, this study will develop a clearer picture of what a grief-conscious graduate student support ecosystem could look like. The project will identify opportunities for strengthening communication, resource awareness, faculty and program-level responses, referral pathways, and mechanisms for supporting students whose academic progress has been disrupted by loss. Ultimately, this project seeks to better understand how grief-conscious systems of support can contribute to graduate student retention, persistence, progression, well-being, and degree completion. While the initial study will focus on KSU, the long-term goal is to develop a model that can inform graduate student support across KSU and potentially be adapted by other colleges and universities.

Metabolic health and well-being are overlooked as a priority for already stressed college adults. Many college students balance coursework, family, and financial obligations, and this may cause health concerns to be minimized. This is especially concerning as many of the lifestyle habits formed during this time may continue into late adulthood. Based on the objectives for the Healthy People 2030, which sets data-driven national objectives to improve health and well-being over the next decade, there is little or no detectible change in the following objectives: increased control of high blood pressure in adults, increase cholesterol treatment in adults, and improve cardiovascular health in adults. Additionally, the objective to reduce stroke deaths has been getting worse. The purpose of this project will involve assessing metabolic health and well-being of college students across time. This is important as recent guidelines have been updated to include earlier screening and interventions for blood pressure, prediabetes, and type II diabetes, but it is unclear whether earlier screening (prior to 35 years old) would be necessary for other health outcomes that may reduce the risk of cardiovascular disease.

Open source software allows anyone to copy, modify, and improve applications used for both personal and research purposes. You can even submit your changes back to the original developers – and if accepted, your work could be used by millions of people all over the world!   In this project, we will make our own contributions to open source software and research. We will start by reviewing bug reports from actual people and exploring how to solve them – then we’ll submit our improvements for everyone to use!   As we gain confidence and build real world development skills, we’ll expand our focus to open source software selected by the student scholars. You will be able to work on improving software tools that you might end up using in your own future research.   In addition, we will explore how to incorporate machine learning research work into existing open source software. We will learn how to design, train, and test an artificial intelligence architecture – and how to improve it based on the results we observe. We will then use the development skills we’ve gained over the semester to build a proof-of-concept plug-in that uses the AI model in image-processing open source software. Finally, we will measure and study its performance in real world usage to take it from theory to a functional feature in the software.

This project engages undergraduate students in a mentored investigation that seeks to contribute new knowledge regarding the capabilities and limitations of artificial intelligence in music creation, music education, and music scholarship. Students will not merely learn about AI technologies; they will design, conduct, and document original investigations using AI music platforms and AI-assisted educational tools. Working with the faculty mentor, students will develop research protocols that evaluate the quality, effectiveness, and musical characteristics of AI-generated outputs. Areas of investigation may include songwriting, composition, music theory tutoring, ear-training materials, and music analysis applications. The project will generate original datasets consisting of AI prompts, musical outputs, analytical observations, evaluation rubrics, error classifications, and comparative results across multiple AI platforms and tasks. Students will systematically collect and analyze these data to identify patterns, strengths, limitations, and emerging best practices. As the project progresses, students will refine research questions, interpret findings, and develop evidence-based conclusions regarding the role of AI in contemporary musical practice and education. Findings will be synthesized into conference presentations and may contribute to future scholarly publications and educational resources. Because students will create original datasets, engage in structured inquiry, analyze findings, and disseminate results to scholarly audiences, the project represents a mentored undergraduate research experience consistent with the Council on Undergraduate Research definition of undergraduate research and creative inquiry.

What if a mushroom could be both a factory and a food? In the BioInnovation Laboratory at KSU, that's exactly what we're exploring. Our research centers on oyster mushrooms (Pleurotus ostreatus), a common, safe, edible fungus , and our ability to coax it into producing valuable materials that benefit both industry and human health. One major focus is MycoMelanin: using mushroom fermentation to produce melanin, the same natural pigment found in human skin, cuttlefish ink, and countless other organisms. Melanin has remarkable properties, it blocks UV radiation, neutralizes harmful free radicals, and even conducts electricity, making it highly sought after in sunscreens, anti-aging skincare, pharmaceuticals, and next-generation electronics. The problem is that natural melanin currently costs around $500 per gram and can only be reliably sourced from wild-caught cuttlefish, creating a fragile and ethically fraught supply chain. Our lab has developed a way to grow mushroom mycelium in a bioreactor tank, encapsulate it in small gel beads, and use those beads as a reusable biological catalyst that converts inexpensive plant-derived ingredients, including extract from velvet beans, into high-purity melanin that is chemically identical to the cuttlefish-derived product. The same bead catalyst can run multiple production cycles, getting more productive with each use. The second focus of this project is mycoprotein, the protein-rich fungal biomass left over after fermentation. Pleurotus ostreatus mycelium is naturally high in protein, low in fat, and rich in dietary fiber and essential amino acids, making it a nutritionally complete ingredient with significant potential as a sustainable food source or food additive. As global demand grows for plant-based and alternative proteins, fungal mycelium offers a compelling answer: it can be grown rapidly on agricultural byproducts, produces no greenhouse gas emissions comparable to livestock, and requires a fraction of the land and water. Our lab is investigating how to optimize mycelium growth conditions to maximize both protein yield and nutritional quality, and how the fermentation biomass can be processed into food-grade ingredients. As a first-year scholar in this project, you will learn sterile laboratory technique, fungal cultivation, and basic fermentation science, foundational skills for careers in biotechnology, food science, and sustainability. No prior laboratory experience is required, only curiosity and a willingness to learn. This is real research with real commercial implications, and your contributions will directly advance both the science and the story.

College students who are neurodiverse often face challenges with communication, social relationships, executive functioning, self-advocacy, and navigating campus life. This research project trains First-Year Scholars to serve as peer mentors who provide support to neurodiverse students at Kennesaw State University. Building on several years of research focused on students with autism, this final year of the project will expand services to a broader neurodiverse population (IRB-FY25-421, Student Liaisons for Autism: Using College Students in Bridging the Communication Gap.) First-Year Scholars will be trained in evidence-based mentoring practices and will work directly with participants to help improve social engagement, communication, organization, relationships, and overall college success. In addition to the peer mentoring model used in prior years, this phase of the study introduces several new components. Student researchers will assist in developing and evaluating AI-supported training modules that target social, communication, executive functioning, life, and career skills. Scholars will also help facilitate social skills groups and support virtual reality-based activities that allow participants to practice real-world interactions in structured and supportive environments. Student researchers will receive training in human-subjects research, mentoring practices, data collection, behavioral observation, qualitative interviewing, and program evaluation. Throughout the project, they will contribute to the design, implementation, and evaluation of interventions while gaining valuable experience in educational research, helping professions, and emerging technologies. This final year represents the largest planned expansion of the project and may result in as many as 80 additional participants across mentoring, social group, AI, and virtual reality components.

Throughout this project students will collect biomarker and nutrition data on athletes from several different sports from three distinct studies.  The first study will evaluate changes in running performance and plasma volume in runners undergoing sodium supplementation. Participants are trained runners randomized to either one week of sodium supplementation or a placebo control group. Participants complete a 10k time trial before and after the supplementation and are asked to refrain from exercise during the week of supplementation. Hydration status, plasma volume, body composition, and dietary intake will be assessed before and after the supplementation to determine the efficacy of the supplement on performance and physiological outcomes. The second study will examine differences in cognition and performance between different types of e-sports athletes and how hydration/nutrition habits influence performance outcomes. Students will conduct similar measures of hydration, fluid intake, and dietary intake of these players over the course of a simulated gaming tournament. Students will also become familiarized with Neurotracker software, designed to assess brain functions such as attention, processing speed, and working memory. Fluid intake of e-sports athletes will be assessed using a smart water bottle which automatically tracks fluid intake. Throughout the tournament, participants will also be randomized to a group using a toilet equipped with a light sensor indicating whether or not they are hydrated based on a urine sample compared to a control group not using the bathroom with this device equipped.  The last study involves an exploration of dietary habits of athletes, including NCAA and team sport athletes. As part of a collaboration with the Center for Human Research in Sport Performance and Wellbeing, students will assist with management and organization of dietary data collected from athletes. Students will also conducting comprehensive literature reviews into the best ways to noninvasively monitor athlete health and the optimal frequency for assessing dietary intake. These investigations will inform the development of protocols in the Center for Human Research in Sport Performance and Wellbeing to best monitor athlete nutrition and health and allow for generation of future research questions from this data. Students will also be encouraged, hours permitting, to engage in other experiences as desired based on ongoing studies within the Center for Human Research in Sport Performance and Wellbeing.

DNA profiling is a powerful tool for human identification in forensic science, including criminal investigations, disaster victim identification, and missing person or kinship cases. However, recovering DNA from bone can be difficult because the calcium-rich bone matrix can trap DNA, while environmental exposure may reduce DNA quantity and quality. This FYSP project is entering its second year and builds directly on the work of last year’s first-year researchers. In Year 1, students successfully recovered DNA from pulverized bone and compared different extraction and purification conditions. Their preliminary results showed that changes in extraction conditions can affect DNA recovery and provided a foundation for further optimization. In Year 2, new first-year students will continue this work by examining factors such as demineralization, incubation time, and extraction conditions. Students will compare DNA yield and quality among different treatments and help identify methods that are efficient, reproducible, and suitable for future DNA profiling studies. Through the project, students will learn laboratory safety, pipetting, solution preparation, DNA extraction, DNA measurement, data analysis, and laboratory documentation. As they gain experience, they will take a more active role in conducting experiments, troubleshooting procedures, and interpreting results. A new feature this year is peer mentoring. First-year students will work with both the faculty mentor and a returning student from this project who is now a sophomore scholar. The peer mentor will help demonstrate laboratory techniques, model good data-recording practices, and support new students as they become comfortable working in a research laboratory. Through this project, students will experience the scientific research process from experimentation and data analysis to communicating their findings through a presentation at research conferences. They will contribute to an ongoing forensic science problem with practical applications in human identification while developing research skills that can support future study and careers in chemistry, biochemistry, forensic science, and related fields.

This project explores how Kennesaw Mountain serves as both a historic Civil War battlefield and a place that continues to shape cultural memory through literature, history, and public interpretation. While the Battle of Kennesaw Mountain is well known for its military significance during the Atlanta Campaign of 1864, the mountain also tells stories about identity, remembrance, and the relationship between people and place. Using an interdisciplinary approach that combines history, literary studies, and public humanities, this project asks how landscapes preserve memory and how writers, soldiers, historians, and visitors have interpreted Kennesaw Mountain over time. The research introduces the concept of the "poetics of place," which examines how physical environments become meaningful through stories, language, memory, and lived experience. For first-year College student, this project will provide hands-on experience with archival research, close reading of historical texts, field observation, and academic writing while contributing to conversations about Civil War memory in Georgia. The idea of the "poetics of place" recognizes that landscapes are more than geographic locations. Places become meaningful because of the stories attached to them. Battlefields, in particular, become spaces where memory, history, emotion, and identity intersect. Examining Kennesaw Mountain through this lens allows students to understand how historical events continue to influence contemporary culture. Because Kennesaw Mountain is located near our campus, it provides an accessible “living classroom” or “living laboratory” where historical documents can be compared with present-day observations.

What if sawdust could become part of the buildings of the future? This project explores how we can use 3D printing to transform wood waste into beautiful and functional architectural building components. By combining sustainable materials, creative design, and robotic fabrication, we'll investigate new ways to reduce waste while imagining how future buildings might be designed and constructed. As a student researcher, you'll be part of a hands-on team that develops custom wood-based printing materials, designs and prints architectural modules, and tests how different patterns, shapes, and connections influence strength, stability, and the way light passes through a wall. Along the way, you'll learn how digital design, material science, and architecture come together through experimentation. You don't need any previous research experience—just curiosity, creativity, and a willingness to get your hands dirty (sometimes literally!). We'll teach you how to prepare printing materials, operate a large-scale architectural 3D printer, document experiments, and evaluate the results. This is a great opportunity for first-year students interested in architecture, engineering, environmental sustainability, design, or anyone who enjoys making, building, and solving creative problems. You'll gain practical experience with digital fabrication, sustainable materials, and collaborative research while working alongside faculty and other students in a supportive laboratory environment. Come help us imagine the future of architecture—one printed layer at a time.

Prototype Living of Tomorrow (PLOT) is a multidisciplinary research and innovation environment at Kennesaw State University where we explore how artificial intelligence, robotics, sensing, biomedical technologies, and intelligent systems can become part of the environments in which people live, work, recover, and interact. PLOT provides a unique home-like research setting where new technologies can move beyond a computer screen or laboratory bench and become part of a functioning physical environment. The research brings together students and researchers from many areas, including artificial intelligence, robotics and autonomous systems, mechanical and electrical design, embedded systems, biomedical devices, computer vision, multimodal sensing, digital twins, edge computing, human-machine interaction, and intelligent environments. First-Year Scholars will join this broader research effort and have the opportunity to contribute to an active project based on current research needs and their developing interests. Projects may include AGICore, our work toward AI-guided real-time coordination of sensors and devices; intelligent-space awareness and digital twins; robotic systems; assistive and rehabilitation technologies; biomedical sensing and devices; autonomous platforms; or new sensing and AI capabilities for the PLOT environment. Students might collect data from cameras, microphones, motion or environmental sensors; develop Python or C/C++ software; integrate a new sensor or embedded device; build or improve a mechanical or electronic subsystem; test a robot; train or evaluate an AI model; investigate how multiple sensors can work together; or measure the accuracy, latency, reliability, and performance of a new system. Students are not expected to arrive with experience in all of these areas. They will begin with guided activities, learn the tools needed for their particular project, and progressively take responsibility for a focused research question or subsystem. Most importantly, this is intended to be genuine research. Students will help build, test, measure, and improve technologies for which the answers are not already known. They will generate and analyze experimental data and communicate what they discover. By the end of the year, each scholar will have made an identifiable contribution to an active research project and will present their work through the Symposium of Student Scholars, with opportunities for continued research, demonstrations, conference papers, and publications.

Doctors read heart signals, like ECGs, to catch disease early, but today's computer programs sometimes struggle to tell subtle, meaningful patterns apart from noise. This project asks whether quantum artificial intelligence, a fundamentally different way of processing information, can separate healthy from abnormal heart signal patterns more clearly than today's standard methods. Quantum computing, artificial intelligence, and healthcare technology are three of the fastest-growing fields in the country right now, each a national priority, and this project sits at the intersection of all three. When a computer learns to spot patterns in a heart signal, it first translates the signal into a kind of map, a space where similar patterns sit close together and different patterns sit far apart. Right now, our lab builds that map using standard geometry, the same math used in most AI systems. Quantum computing offers a fundamentally different kind of geometry for that map, one that behaves in ways ordinary computers can't replicate. The question is simple: does this quantum version of the map make healthy and abnormal heart patterns easier to tell apart? Students will build both versions and directly compare how well each one separates real patient data, then move into quantum-based deep learning, a more advanced kind of pattern recognition. As a Spring goal, students will extend a method our lab uses to reduce false alarms, flagging a healthy patient as sick, and missed cases, letting a real problem go unnoticed, into this new quantum setting, teaching the model to recognize when it's unsure rather than guess. Students will get hands-on experience across three fast-growing, nationally important fields at once, no prior background required. Working with real, de-identified heart signal datasets from thousands of patient recordings already used in our lab, students will learn the fundamentals of quantum computing and machine learning, then build and compare classical and quantum models. This project builds directly on an active, NSF-funded research program in our lab on trustworthy AI for heart signal analysis, so students join real, ongoing research rather than a project built just for this program. Students will also present their work at KSU's Spring Symposium of Student Scholars, submit a research abstract to the National Conference on Undergraduate Research, and contribute to a research paper on which they will receive authorship credit. Along the way, they will connect with cross-institutional research collaborators, gaining experience in how researchers work across universities.

Quantum circuit simulations are essential for verifying quantum algorithms on real quantum devices. However, the memory requirements for such simulations grow exponentially with the number of qubits involved in quantum programs. Moreover, a substantial number of computations in quantum circuit simulations cause low-locality data accesses, as they require extensive computations across the entire table of the full state vector. These characteristics lead to significant latency and energy overheads during data transfers between the CPU and main memory. Processing-in-Memory (PIM), which integrates computational logic near DRAM banks, could present a promising solution to address these challenges. In this project, we will implement QuantumPIM to achieve fast, energy-efficient quantum circuit simulation.

Machine Learning is known to provide solutions for data analysis and interpretation, and it is used in various fields such as computer vision, malware detection, and drug discovery. However, traditional machine learning approaches are incapable of successfully extracting useful information from large data sets, as they require tremendous time and resources while being performed on traditional computers. Quantum computing is a new type of qubit-enabled computing paradigm based on quantum properties such as superposition, interface, and entanglement for data processing and other tasks. It can be used to work on problems traditional supercomputers would not be able to handle efficiently. Quantum Computing can collaborate with Machine Learning for faster computation and more accurate data analysis, and Quantum Machine Learning (QML) has gained a lot of attention from both academia and industry recently.   Two of the most important applications of QML are cybersecurity and analyzing data from various science and engineering fields, such as biology and industrial engineering. This project will begin with learning Machine Learning and Quantum Computing, then followed by the development of a system in Python (developed in Google Colab) that uses the Quantum Tensorflow package and applies QML algorithms to process various (1) security and malicious data sets (2) science and engineering data sets and compares the performance with classical Machine Learning (CML) algorithms. In this project, the students will also conduct research on various quantum platforms such as Microsoft Quantum Development Kit, IBM Qiskit, Google AI Quantum Cirq, PennyLane, and more.

The rat lungworm, Angiostrongylus cantonensis, is a parasitic nematode that causes severe neurological disease in humans and wildlife. Rat lungworm has a complex life cycle that uses rats as definitive hosts and gastropods (e.g., snails and slugs) as intermediate hosts. The parasite's infectious stage is the third larval phase, which occurs in the gastropod host. Humans and other wildlife are infected after ingesting raw gastropods, of which more than 200 species are known hosts of rat lungworm. Rat lungworm was first described in China in 1935, but has since invaded all continents except Antarctica. In Atlanta, rat lungworm was first detected in rats in 2023, but its current prevalence and distribution, especially in gastropods (the disease vector), in northern metro Atlanta is unknown. Furthermore, gastropod biodiversity in Georgia is substantial (estimated at 300-plus species), which increases the probability of rat lungworm emerging as a threat to public and wildlife health in Georgia.  This project aims to establish 1) rat lungworm prevalence and distribution in the northern metro Atlanta area and 2) identify the most common gastropod species infected with the parasite. This will involve collecting, and identifying, gastropods from northern metro Atlanta as well as extracting rat lungworm from collected gastropods. Students working on this project will gain experience in field research, gastropod collection, parasite extraction, microscopy, and molecular and bioinformatics workflows.

Arsenic is one of the most persistent and ubiquitous environmental toxins. To overcome this problematic element, life has evolved and acquired a number of arsenic detoxifying mechanisms. Bacteria, due to the immense environmental adaptability and biochemical versatility, have even flexibly devised various ways to utilize arsenic for biological functions such as energy production, osmotic adjustment, phosphate sparing, etc. Our recent studies indicate a new way of bacterial arsenic utilization – offensive weapons. Notably, bacteria wage “arsenic warfare”, where some members weaponize environmental arsenic, synthesizing arsenic-containing antibiotics to kill neighboring competitors, while others develop countermeasures against the arsenic weapons. This new emerging “bacterial arsenic warfare” concept provides a new dimension to understanding the arsenic biogeochemical cycle and brings new perspective to environmental arsenic biochemistry, as well as leads to discovery and development of new and potent antimicrobials. In this project, students will investigate how bacteria weaponize environmental arsenic using 1) prospective bacterial strains that possess novel gene(s) involved in arsenic metabolism/transformation, 2) a genetically manipulatable bacterial strain (Escherichia coli) engineered with the novel gene(s), and/or 3) purified protein(s) encoded by the novel gene(s). Alternately, students may investigate antimicrobial property of known organoarsenicals. While the project primarily targets arsenic, it may deal with other similar metals and metalloids (e.g., antimony, selenium, etc.). The expected outcomes are identification and characterization of 1) novel arsenic-containing antibiotics, 2) novel genes/proteins that carry out novel arsenic biotransformation, and/or 3) antimicrobial properties of other organometallic compounds. The dramatic increase in bacterial resistance to antibiotics is a grave threat to global health. A dearth of new antibiotics has fostered the emergence and spread of drug-resistant bacteria, resulting in an increase of serious infections with high mortality rates. To overcome this serious health concern, discovery and development of new antibiotics are urgently needed. The future and long-term goal of this project is to demonstrate the potentials of arsenic-containing antibiotics to establish a new pipeline for our shrinking antibiotic arsenal.

This project will explore how AI models, such as small language models, can orchestrate a team of different types of robots to work together efficiently. Imagine a team of robots collaborating on a mission described in natural language: one robot explores the environment, another picks up an object, and another transports it to its destination. How can these robots decide task assignments, communicate with one another, and complete the mission efficiently without relying on a powerful computer or unlimited battery? Students will work with real robotic platforms, including wheeled, legged, and manipulator-equipped robots, and investigate how small AI models on the edge can understand tasks, make plans, and coordinate their actions. A particular focus will be on resource- and energy-aware intelligence: enabling a robot team to decide not only what to do, but also how to accomplish a mission using constrained computing power, time, and battery energy. Students will participate in hands-on activities such as programming robots, developing agentic AI models, software-hardware codesign, designing simple collaborative missions, collecting experimental data, and comparing different AI and planning strategies. As the project progresses, students may also explore how robots can respond when something unexpected happens—for example, when a robot cannot complete an assigned task or the environment changes. Students will learn the necessary concepts and tools through guided research activities while gaining experience with programming, AI, embedded computing, experimentation, and teamwork. The project is especially suitable for students interested in robotics, artificial intelligence, computer or electrical engineering, and autonomous systems who would like to experience research early in their college career.

Protons are fundamental particles that drive critical processes in nature. They enable energy production in biological cells, govern chemical reactions in atmospheric clouds, and power advanced materials like fuel cells. To understand these real-world processes, scientists must observe how protons travel from one molecule to another. However, studying this movement directly in a lab is exceptionally difficult because protons are extremely small, light, and fast-moving. Therefore, Kaledin’s research group addresses this challenge by using high-performance computing (HPC) resources at KSU to run "virtual experiments" or molecular simulations. We use specialized software (Gaussian 16/Gaussview/Molpro) to build 3D models of water clusters and functionalized molecular systems bound together by a single proton. By applying the laws of quantum mechanics and physics, we simulate the exact step-by-step motion of atoms over time. These dynamic simulations allow us to watch how protons jump between molecules, how surrounding chemical structures flex and bend to assist that transfer, and how temperature affects their overall mobility.      A major focus of our work is predicting spectroscopic signatures, in particular infrared (IR) and Raman spectra. These spectra act like "fingerprints" when detected. Molecular complexes absorb or scatter light, and they vibrate at specific frequencies. By computing these vibrations, we can generate simulated spectral fingerprints that directly assist experimental scientists in identifying complex molecular structures.      This project will involve students from diverse backgrounds who are interested in learning how computing can address fundamental chemical questions. First-year scholars will receive comprehensive training in scientific software, basic programming scripts, chemical visualization, and data analysis. By translating abstract quantum concepts into interactive computer models, students will help bridge the gap between theoretical calculations and real-world scientific observations, contributing to data used in biophysics, materials science, and atmospheric chemistry.

Imagine if your phone or smartwatch could run advanced Artificial Intelligence (AI)—like recognizing voice commands or detecting security threats—without draining its battery in minutes or constantly sending your private data to cloud servers. Biological brains excel at this balance: they process massive amounts of sensory information using very little energy because they only activate when something new or significant happens. Standard computers, on the other hand, consume significant electrical power because they process every piece of data continuously, whether anything changes or not. In this project, we explore neuromorphic computing, a cutting-edge area of AI designed to mimic the event-driven energy efficiency and structural wiring of biological brains. Using a novel brain-inspired computer chip called the BrainChip Akida AKD1000 connected to a palm-sized Raspberry Pi 5 micro-computer, students will build and test a physical prototype of an ultra-low-power, brain-inspired sensing device. First-year scholars will learn how artificial "spiking" neural networks process real-world signals (such as sound keywords or visual motion patterns) in real time. Students will gain hands-on experience assembling compact edge hardware, training lightweight AI models, and benchmarking how efficiently these brain-like chips process data compared to standard computer processors. This project bridges hardware, computer science, and smart electronics to offer students an engaging, accessible entry point into the future of green, private, and energy-efficient AI.

Every school morning and afternoon, long lines of cars form around Georgia's elementary and middle schools. Since the pandemic, many more parents drive their children to school instead of using the bus, and families enrolled in Georgia's school choice program often have no other option. The result is heavy congestion at the intersections near schools for about an hour twice a day: wasted time for parents, extra pollution from idling vehicles, and added safety risk for children who walk or bike. This project continues multi-year work in our research group aimed at making these trips safer and faster. This year the team focuses on two things. First, students will build a "digital twin" of the intersections near a selected school. A digital twin is a realistic computer model of a real place, in this case the streets, signals, and vehicles, that behave the way the real location behaves. Students will collect field data (traffic counts, signal timing, and street geometry) and use free, open-source simulation software called SUMO to recreate the school zone on a computer. Once the model matches what happens on the street, the team can test ideas safely and cheaply before anything changes in the real world: new signal timings, different drop-off routing, or staggered dismissal. Second, students will develop a smartphone app prototype that tells parents how long the pick-up wait currently is, when to leave home, and which approach to the school is moving best. The app will draw on the digital twin so that its recommendations are grounded in simulated, and eventually real, traffic conditions. No prior experience is required. Students receive structured training in every tool they use, including guided training on the responsible and effective use of AI tools for coding, data handling, and technical writing. Because the work spans data collection, simulation, and app development, the project fits students from civil, electrical and computer, mechanical, and computer science backgrounds, and each Scholar can steer toward the tasks that interest them most. First-Year Scholars will not work alone. They join an active research team that includes the faculty mentor and a graduate student completing a larger related directed study (CE 8850) on digital twins of campus-adjacent intersections. Scholars will see how a research project runs from question to publication, and their own work will be a real part of it, ending in a presentation at the Spring Symposium of Student Scholars.

Want to gain real research experience during your first year at KSU while exploring issues that shape college campuses and public life? This project offers students from any major the opportunity to join an active research team studying free speech, civil discourse, and political polarization in higher education. College campuses bring together people with different backgrounds, beliefs, and political perspectives. Our research examines how students, faculty, and staff experience conversations across these differences, how they understand the campus environment, and what institutional conditions encourage or discourage constructive engagement. The research is already underway, so participating students will have the opportunity to contribute to an established project with real data and potential scholarly and public impact. Students will receive step-by-step guidance as they assist with reviewing scholarly literature, organizing and analyzing qualitative data, identifying patterns and themes, and developing conclusions supported by evidence. Students will also help prepare research products such as a poster, conference presentation, research brief, and manuscript for potential publication. By participating, students will develop transferable skills that are valuable across majors and careers, including critical thinking, research, data analysis, teamwork, professional communication, and presenting complex ideas clearly. They will learn how a research project moves from an important question to findings that can inform universities and community partners. Students will present their work at KSU’s Symposium of Student Scholars and may have opportunities to contribute to additional presentations or publications. The project is part of a broader faculty research agenda exploring dialogue, trust, civic engagement, and democratic resilience. Findings may also inform the education-related work of the Georgia Democracy Resilience Network, a statewide initiative led by The Carter Center. No previous research experience or specialized knowledge is required. Students from all majors and perspectives are encouraged to apply. We are looking for students who are curious, dependable, open to learning, and interested in understanding how people and institutions navigate difficult conversations. This is an opportunity to begin building your research skills, professional relationships, résumé, and academic confidence during your first year at KSU.

Imagine a material a thousand times thinner than a human hair, yet strong enough to bounce back after being squished flat over and over again. That's a carbon nanotube sponge: a lab-grown, ultra-lightweight material built from millions of microscopic carbon tubes tangled together like steel wool, but at the nanoscale. These sponges are being explored for things like spacecraft insulation, flexible electronics, and shock-absorbing materials, anywhere you need something light, squishy, and good at managing heat. But here's the question nobody has really answered yet: what happens to these materials after they get squished again and again? Does repeated stress quietly wear down their ability to conduct heat, even if they still look and bounce back the same? That's exactly what this project will investigate. Working directly in a research lab, you'll help repeatedly compress carbon nanotube sponge samples using specialized testing equipment, then measure how well they conduct heat before and after. You'll also use a high-powered microscope to look inside the material and see how its internal structure changes after being stressed. No prior lab experience, coding skills, or specific major is required. You'll be trained on all the equipment and techniques from day one. This project is a great fit for students curious about materials science, mechanical engineering, physics, or nanotechnology — but really, anyone interested in hands-on lab work and discovering something genuinely unknown is welcome to apply. You'll work closely with the faculty mentor and current graduate/undergraduate researchers, building real experimental skills while contributing to original research. By the end of the year, you'll have generated real data, learned to operate research-grade instruments, and presented your findings at KSU's Spring Symposium of Student Scholars.

Dementia is a growing healthcare concern. More than 57 million people worldwide are living with dementia, with nearly 10 million new cases each year. As the population ages, this number is projected to reach approximately 139 million by 2050. Nurses will therefore increasingly care for patients with dementia who may experience memory loss, confusion, changes in behavior, fear, or difficulty communicating. However, currently, nurses and nursing students may receive limited specialized training in dementia care, especially training that helps them understand what it may feel like to live with dementia and develop patient-centered approaches to communication and care.  One promising educational tool for dementia-care training  is virtual reality (VR), which can place learners in realistic situations. This research project explores whether VR and artificial intelligence (AI) can strengthen dementia-care training and improve nurses’ empathy toward people living with dementia. Nurses at a local hospital will participate in an immersive VR simulation designed to provide a first-person perspective of experiences associated with dementia, followed by guided reflection. Participants will be assigned to one of two groups. One group will complete the VR simulation and standard guided reflection, while the second group will complete the same activities followed by additional reflection with an AI assistant. The study will examine whether the VR-based dementia training is feasible and acceptable in a hospital setting. It will also examine whether participating in the training improves nurses’ empathy, attitudes toward dementia care, patient-centered communication, and preparedness to care for people living with dementia. The study will also investigate whether adding AI-supported reflection provides additional benefits beyond the VR simulation and standard reflection alone. Findings from this research will help determine whether combining VR, guided reflection, and AI offers a practical and effective approach for strengthening dementia-care training and better preparing nurses to understand, communicate with, and provide patient-centered care for people living with dementia.

Problem: The growing diabetes epidemic affected more than 30 million Americans in 2017; with a staggering economic cost of $327 billion in the United States alone. Among the many risk factors, studies have shown that excessive consumption of sugars is the main cause of type 2 diabetes and is implicated in many medical problems such as cardiovascular disease, obesity, and even some cancers. Rationale: Sweeteners derived from plant natural products have significant potential as dietary supplements because they are stable and non-caloric. More importantly, natural sweeteners could help patients who are diabetic, phenylketonuric (inability to break down an amino acid called phenylalanine), and/or obese reduce their sugar intake. Stevia is a natural high-intensity sweetener isolated from leaves of Stevia rebaudiana, a tender perennial herb native to semitropical regions of South America (e.g., Paraguay and Brazil). The leaves of this plant contain more than ten ent-kaurene diterpenoid glycosides composed of a steviol aglycone decorated with different numbers and types of sugars. Commercially available steviol glucosides have the characteristic bitter aftertaste of specific types of steviol glucosides, thus preventing widespread commercial use. The identification of new, natural, and low/non-calorie sweeteners and research of their biosynthetic pathways are essential to addressing numerous health issues. Goals & Activities: The research objective is to manipulate the pattern of glycosylation and to improve the yield of desirable stevia compounds using 3D structure-guided protein engineering and mutagenesis techniques. The First-Year Scholars Program will support our biochemical experiments to understand how Stevia plants form various natural products and to alter essential enzymes in Stevia to generate new versions of noncaloric sweeteners by employing structure-guided protein engineering techniques.

What will cities look like 30 years from now? What is the future of building materials? This research project seeks to bridge the gap between material science and construction, looking at sustainable approaches to designing new buildings and infrastructures. It can take decades for a breakthrough in engineering from a lab to a building site. This research embraces the need for innovative architectural forms while building upon structural design principles to create a new generation of smart materials.  Students working on this research project will investigate the use of sustainable approaches in building materials of the future to (1) achieve more economical construction, (2) improve sustainability and resilience, and (3) advance architectural forms and forces. The goal is to advance our fundamental understanding of cementitious materials and their construction in an effort to marry architectural form and structural design. The last hundred years in architecture and civil engineering have been widely dominated by the use of concrete, which became the second most consumed commodity after water. Although concrete and cementitious materials have a low embodied energy (approximately 0.90 MJ/kg), they are used in vast quantities. In 2019, cement production amounted to approximately 3.2 billion tons, with production and usage accounting for almost 8-9% of total global anthropogenic greenhouse gas emissions.  This research aims to impact the embodied energy and the carbon emission associated with new concrete constructions by possibly saving up to 30% in concrete volume compared to an equivalent strength prismatic member. This research thus offers exciting opportunities for engineers and architects to move towards a more sustainable construction industry.

This project will engage first-year undergraduate students in exploring how teacher preparation programs are responding to the growing use of generative artificial intelligence (GenAI) in education. As AI increasingly becomes part of lesson planning, assessment, writing, and instructional decision-making, future teachers need more than technical knowledge. They need to learn how to collaborate with AI while preserving human judgment, creativity, voice, ethical reasoning, and professional responsibility. Accordingly, the proposed research seeks to investigate how educator preparation programs currently foster AI readiness among preservice teachers across multiple institutional contexts. Specifically, the study examines interactions among preservice teachers, faculty members, institutional policies, curricular materials, and program structures to better understand how AI readiness is developed within teacher education. By comparing educator preparation programs representing different institutional missions and organizational contexts, the study will generate evidence regarding how AI is formally incorporated into teacher preparation, how hidden curricular messages influence professional learning, and how faculty members perceive their responsibility for preparing AI-ready educators. The student researcher will contribute to the larger Teaching for Tomorrow with AI (T4T-AI) project, which conceptualizes AI readiness as including AI literacy, pedagogical capacity, ethical and responsible AI use, and authentic opportunities to practice AI-supported teaching. The student will focus specifically on the “human-AI collaboration” dimension by examining how teacher preparation courses communicate what aspects of teaching and writing should remain distinctly human. The students will conduct a structured analysis of selected course syllabi, assignments, AI policies, interview data of faculty, students and admins and faculty guidance documents to identify how programs communicate expectations regarding AI use and will participate in student and faculty and administrator interviews with Bagwell within KSU. The student will categorize messages related to AI as a tool, collaborator, or replacement, with particular attention to writing, creativity, critical thinking, professional judgment, and ethical decision-making. This work will build on preliminary findings from KSU showing that AI is currently addressed primarily through academic integrity and restrictions, while relatively few courses provide opportunities for teacher candidates to develop practical skills for using AI in educational settings. % students, 5 faculty, 4 program coordinators and 2 administrators within Bagwell interview with in KSU. This project is already IRB approved and PI Maitra is gearing for data collection. This will be a great platform for the first year scholars to be mentored by PI Maitra and her GRA Karissa Kimble .

Augmented Reality (AR) smartglasses let us see digital information directly in our line of sight. Imagine walking outdoors while navigation directions, maps, or helpful tips about your surroundings appear seamlessly in front of you. Recent advances are making AR smartglasses more practical than ever: lighter designs make them comfortable to wear for longer periods, Vision Positioning Systems (VPS) provide centimeter-level tracking accuracy so your headset knows exactly where you are, and powerful artificial intelligence (AI) acts like a “second brain,” ready to assist with everyday tasks. This project, not limited to labs, will explore how to use the latest AR headsets for outdoor, on-the-move, real-world scenarios. We will combine technologies like VPS, AI (including computer vision and AI agent), and creative interaction techniques, along with elements of game design, to create engaging and useful applications. You might: • Design AR navigation that gives hands-free, real-time directions to your destination • Build interactive field guides to explore historical landmarks, plants, or wildlife • Create lightweight interaction systems that support efficient information access while on the move • Invent collaborative outdoor games where the entire campus becomes your game board As a first-year scholar on the project, you will have the opportunity to design, prototype, and test AR applications that go beyond the lab with various populations. You will work with cutting-edge AR devices, learn about spatial computing, experiment with interaction methods, and explore how AI can make AR experiences more personalized and intelligent. You will also test your prototypes with real users and collect data to evaluate your designs using psychology-based research methods. The goal is to discover what today’s AR technology can (and can’t) do in real-world situations and to imagine new ways it could enhance daily life. Along the way, you’ll gain hands-on experience in creative problem-solving, teamwork, user experience design, and emerging technologies that are shaping the future.

The popularity of mindfulness and mindfulness-based programs has ballooned over the last two decades, with applications spanning therapy clinics, hospitals, and mobile devices. These programs are based on ancient contemplative and Buddhist traditions, and therefore teach concepts originally associated with such traditions, including non-attachment, equanimity, and skillful responses to craving. The developers of the first Western adaptations, most notably Mindfulness-Based Stress Reduction (MBSR) and Mindfulness-Based Cognitive Therapy (MBCT), intentionally distanced these programs from their spiritual and religious roots in hopes of wide dissemination in Western medical and clinical settings. Unfortunately, this disconnection between the roots of mindfulness and how it is now taught may have eroded the semantic richness of these ideas, which might have been preserved had they been captured metaphorically from a Buddhist perspective. In short, mindfulness may have lost some of its depth in translation. This project examines whether teaching metaphors steeped in Buddhist and contemplative tradition communicate mindfulness concepts more effectively than the standard explanations found in the curricula of MBSR, MBCT, and related programs. We are collaborating with two ordained Theravada Buddhist monks, Venerable Michael and Venerable Nick, who have taught meditation to thousands of practitioners and developed a rich compendium of teaching metaphors. For example, the monks teach that the agitated mind is like a glass of muddy water: stirring keeps it cloudy, while allowing it to settle brings clarity. Mindfulness is presented as settling rather than forcing. First-year scholars will help build and run a randomized online experiment (N = 500; approximately 150 analyzable per condition) with three conditions: standard explanations extracted from MBSR and MBCT curricula, monk-derived metaphors, and monk-derived metaphors paired with AI-generated illustrations. Across six core mindfulness concepts, we will measure perceived clarity, usefulness, memorability, respectful tone, motivation to practice, and objective understanding using knowledge and application questions. Working with the PI and a senior-year honors research assistant, scholars will help catalogue how these concepts are taught in MBSR and MBCT curricula, organize the monks' metaphor compendium, draft study stimuli and survey items, assist with the ethics application, collect and analyze data, and present findings at the Symposium of Student Scholars. Scholars will also meet the monk collaborators and learn how rigorous, respectful research is built with knowledge holders outside academia. Findings will inform how mindfulness is taught in clinical and community settings and generate preliminary data for external grant applications in mental health and addiction recovery contexts.

When structural safety is denied, the deliberate cultivation of joy becomes both a political and social necessity. It functions as an empirical indicator of community resilience and alternative resource generation. Social scientific examinations of the Black community have often focused only on deficit models, including inquiries into the harms of systemic racism, segregation, and socio-economic exclusion. This work catalogs Black trauma and stress but overlooks the active strategies communities use to survive and flourish. This project addresses that gap. Building on my work in the article “Finding Black Joy in a World Where We are Not Safe” and my book, Bodies Out of Place: Theorizing Anti-blackness in U.S. Society, this study investigates Black Joy not as a passive emotion, but as a deliberate, collective tool used to navigate and resist structural oppression. By analyzing how individuals define, locate, and project joy, the research shifts the analytical lens from purely documenting systemic injury to evaluating the micro-sociological strategies of survival. The proposed project investigates how marginalized Black college students cultivate joy as a form of agentic resistance to spatial precarity caused by systemic racism. The project seeks to capture the everyday lived realities of resistance. Utilizing primarily a visual sociology approach, the research employs three complementary methods to create a robust framework for explaining and understanding of the concept. They include: 1) critical textual analysis of common readings, 2) experiential learning through 2-day immersion visit to Equal Justice Institute (EJI) museum sites in Montgomery, Alabama and 3) creation, implementation, and analysis of a photovoice project to examine how Black communities create, experience, and protect joy as a tangible socio-emotional resource amidst structural inequality.

The Developmental Research Collective (DRC) is a research lab within the Department of Psychological Science. We investigate how infants and children make sense of the world around them, and how parents support growth throughout the developmental process.  We are currently investigating the play activities between parents and their young children. This study focuses on the differences in play interaction between maternal and paternal primary caregivers. Through semi-structured activities, we will be able to examine family dynamics through five types of interactive play. The objective of the project is to evaluate the commonalities as well as differences in parent-child play when the parent is male versus female. We are especially interested in how parental gender influences their play style and how their children respond accordingly.

Imagine being able to control a tiny amount of fluid through channels smaller than a strand of hair, or using a magnetic field to move part of a biomedical device without physically touching it. That is the basic idea behind this project. Our research focuses on developing small, electronically controlled microfluidic devices that could eventually be used for applications such as sensing, diagnostics, and drug delivery. A First-Year Scholar joining this project will have the chance to work on an actual device that we are designing and testing in the lab. There are several parts to the project. We will make silicone microfluidic channels, experiment with microneedles, build and test small electromagnetic coils and circuits, and use sensitive electrical instruments such as a nanovoltmeter to measure very small signals. We will also use COMSOL Multiphysics to simulate the device and see what is happening with magnetic fields, forces, and fluid flow that we cannot easily see during an experiment. We may also design and 3D-print parts for our experimental setup. One of the things I enjoy most about this research is the connection between simulation and something you can actually build and hold in your hand. We can predict what should happen in COMSOL, build the device, measure what really happens, and then try to understand why the two agree—or why they don't. Students from different backgrounds are welcome. This project brings together biology, chemistry, electrical and mechanical engineering, and physics. Depending on your interests, you might find yourself working with microfluidic channels and microneedles, measuring tiny electrical signals, studying magnetic fields, running simulations, or designing and building parts for an experiment. No previous research experience is necessary. I don't expect a first-year student to walk into the lab already knowing how to do any of this. We will learn these things along the way. What matters much more is curiosity, patience, reliability, and a willingness to try something, figure out why it didn't work, and try again. The goal is to generate real experimental data during the year, present the work at the Spring Symposium of Student Scholars, and develop the results toward a research publication in Spring 2027.

Large language models (LLMs) are being rapidly adopted across many fields, including education, business, research, and healthcare. Their growing use to answer health questions and provide educational information creates new opportunities to support patients and caregivers. However, medical information can influence important care decisions, and AI-generated responses may be incomplete, inaccurate, or misunderstood. Patients and caregivers with different levels of health literacy may find it difficult to determine whether information is trustworthy, has been reviewed by a healthcare professional, or should be confirmed before it is followed. Transparency, appropriate guidance, and human oversight are therefore especially important when LLMs are used in medical settings. When AI-generated medical information is copied, summarized, or shared outside the original system, its source may no longer be clear. A watermark, which is a hidden pattern added to AI-generated text, could help identify whether the information originated from an AI system. However, text watermarking often works by influencing the words selected during generation. This process may change the original response and alter the information being communicated. In medical contexts, even small changes to medication names, numbers, symptoms, negative expressions such as not, or recommended actions could significantly affect the meaning of a response. Watermarks may also become difficult to detect when a response is shortened, translated, summarized, or rewritten. The goal of this project is to evaluate whether existing watermarking methods can reliably identify AI-generated medical information while preserving the meaning of the original response. Specifically, the project will 1) create a dataset of medical questions and generate paired responses with and without watermarks; 2) compare these responses to determine whether watermarking changes important information, such as medication names, numbers, symptoms, negative expressions, or recommended actions; and 3) test whether the watermarks can still be detected after the responses are shortened, paraphrased, summarized, translated, or converted from text to speech and back to text. The project will result in a medical watermarking benchmark, a systematic evaluation of current watermarking methods, and practical recommendations for using watermarks in medical LLM applications.

Recent and rapid advances in Artificial Intelligence (AI), particularly in the form of deep neural networks, has opened many new possibilities, but it has also brought with it many new challenges. In particular, it has become increasingly apparent that while deep neural networks are highly performant, they can also be opaque and brittle. We do not have enough understanding of why and when they work well, and why they may fail completely when faced with new situations not seen in the training data. Over the past few years, our research group has developed a symbolic approach to explaining and formally verifying the behavior of machine learning models. More recently, we have developed an algorithm that can train a neuron from data in such a way that it facilitates its own explanation and formal verification. Our goal now is to generalize this idea so that we can train an explainable neural network.

Online conversations are constantly changing. A topic that receives little attention one week may suddenly become widely discussed after a major event, while a community that once appeared unified may split into smaller groups with very different viewpoints. This project will explore how these changes can be identified using real-world social media data. The student will follow one or more online conversations centered around topics such as sports, entertainment, brands, technology, or current events and examine how the structure of those conversations evolves over several weeks. Rather than looking only at the number of posts or mentions, the project will represent online discussions as networks, where users, topics, or communities are connected based on how they interact or appear together. The student will create network snapshots at different points in time and compare them to investigate questions such as: Does the conversation become more connected or more divided? Do new communities suddenly emerge? Are there moments when the structure of the discussion changes substantially? Do these shifts coincide with major announcements, news stories, controversies, or other real-world events? The student will learn how researchers turn raw social media data into meaningful visualizations and simple measures that describe the structure of an online community. As the project develops, the student may also explore introductory methods for identifying important change points and examine the topics or themes associated with different online groups. No prior experience with programming, statistics, or network science is required. The project is designed as a hands-on introduction to data science research, and students will receive guidance and starter code throughout the process. By the end of the project, the student will create a research poster that tells the story of how an online conversation evolved over time. Strong findings may also form the basis of a conference paper and provide opportunities for continued research in social media analytics, network science, and dynamic data analysis.

As a road user, have you ever been involved in a motor vehicle crash? Do you know anyone who has been involved in a crash? Have you ever seen crashes when you travel either as a driver or a passenger? Traffic safety is a big concern in Georgia, and the number of crashes (also known as accidents, wrecks, etc.) and associated fatalities and severities have increased considerably in recent years. The economic cost associated with these crashes is in the range of billions of dollars every year. It is important for Georgians to look at the traffic safety situation and find ways to make it better.  When looking at ways of improving traffic safety, one of the effective strategies is to look at special population groups separately and try to identify the ways to improve. Among many such groups that are over-represented in terms of the number of crashes, young drivers are a noticeable group. Among many factors, young drivers have less experience in driving and are more likely to use technology while driving, creating significant safety risks to themselves, and other road users. Despite efforts by the Georgia Department of Transportation (GDOT), and other agencies, young-driver-related crashes remains at high levels which is alarming. It is therefore important to study what factors are associated with traffic safety of this population group. This study analyzes crash data to identify trends, patterns, and contributing factors related to young driver-related crashes in Georgia. The research will further investigate the relationship between crash occurrence and severity, with the goal of developing recommendations to enhance road safety of young drivers.

The THOR project includes multiple studies within the Applied Human Performance portfolio of the Center for Advancement of Military and Emergency Services Research (AMES Research). This portfolio investigates systemic barriers to exercise, evaluates the impact of occupational health policies, delivers fitness and nutrition education to local first responder agencies, and works toward a broader goal of improving military and first responders' physical fitness, resilience, and long-term health. Research assistants on this project will contribute to ongoing and emerging research focused on emergency services personnel, including topics such as occupational fitness standards, physiological stress and performance, injury risk reduction, and long-term health outcomes. Specific tasks may include writing and editing, data organization, and data analysis, depending on project needs and each student's interests and strengths. Specific research topics currently available for student subprojects include:  • Influence of physical fitness metrics on firefighter occupational performance • Remote monitoring of firefighter safe work rate during occupational tasks • Law enforcement cadet wellness metrics (e.g., stress and fitness) Each student will have the opportunity to take ownership of a specific piece of the larger research effort. This allows them to develop skills in that area and see how their individual contribution supports the broader goals of the project.

Several changes have impacted how K-2 mathematics and literacy are taught in the last few years. Most recently, Georgia passed the Math Matters Act (HB 1030), which goes into effect for the 2027-2028 academic year. Although the legislation in this act is targeted for upper elementary, middle, and high school students, the effects will be felt with our PreK-2 learners. Sound mathematics and literacy instruction are essential for PreK-2 learners.    The Math Matters Act centers “evidence-based” instruction and “emphasizes the development of students’ mathematical proficiency through a balanced focus on foundational skills, deep conceptual understanding, and meaningful application of mathematics. Evidence-based mathematics instruction engages students in problem solving, mathematical reasoning, discussion, and justification of strategies while fostering confidence in their ability to learn and use mathematics effectively.” (GADOE, 2026, p. 3). The Math Matters Act follows years of legislation related to reading, which has polarized teachers and faculty alike, as many argue that the focus on structure, the science of reading, and universal screening have been used to reduce teachers' and students' choices in the kinds of books available in their classrooms.   The focus of this project is threefold. First, participants will have the opportunity to learn about the content, equity, and pedagogical principles of PreK-2 mathematics, as well as the range of evidence-based instruction that has shaped those principles in recent years, through structured literature reviews. Second, participants will use the Teaching Tolerance Anti-Bias standards (IDJA–Identity, Diversity, Justice, and Action) to review and select inclusive picture books. Finally, participants will develop and pilot a protocol for using picture books alongside high-quality mathematics instruction to create learning experiences that are mathematically rich, literacy-supportive, culturally and linguistically responsive, joyful, and accessible to every child.   The central research question participants will investigate is: What characteristics of children's picture books and instructional approaches support rich, contextual, rigorous, joyful, and inclusive mathematics experiences for PreK–2 learners?

This project explores whether psychological tendencies such as intolerance of uncertainty and repeated checking are associated with greater dependence on generative AI among college students. As tools such as ChatGPT, Gemini, and Copilot become part of students’ academic and everyday routines, some students may rely on them more heavily than others. Some questions that we plan to explore: • Are students who are less comfortable with uncertainty more likely to develop greater AI dependency? • Is repeated checking associated with stronger reliance on generative AI? • Does trust in AI strengthen these relationships? • When unsure about an AI answer, are more dependent students more likely to ask AI again rather than verify the information independently? The study will survey college students to examine how uncertainty, repeated checking, and trust in AI relate to AI dependency. This interdisciplinary study aims to improve our understanding of psychological factors associated with AI dependency and verification behavior among college students.

AI usage has been steadily increasing in brand advertising. Several brands like Coca Cola, The Home Depot and FIFA have used AI in their advertisements. While AI usage reduces costs, increases efficiency and speed, and creates time for other activities for brand managers, it also has received widespread coverage ranging from positive to negative reactions. It also has a societal impact (in terms of affecting creative jobs) and also creates content that looks similar and often times, mediocre. Regardless, firms have adopted several AI tools like Canva AI, Claude, Kling, Veo, etc. At this point, the key concern is whether the increasing cost of AI and its diminishing returns are actually relevant for usage in developing creative content. While it is still too early to take a stance on the use or non-use of AI in advertisements, much more work needs to be done to understand in which contexts customers react negatively and/or positively/neutrally to blatant AI usage in advertisements. Towards this end, the current study examines the impact of AI usage on brand advertisements (ad copies). Specifically, we seek to understand a) how do consumers respond to AI in advertisements, b) to what extent is the AI use in brand advertisements explicit, and c) the backlash against AI usage in advertisements and its downstream consequences.

Analysis of the spatial organization of cell nuclei in 3D microscope images provides an opportunity for researchers to gain a deeper understanding of tissue structure and to investigate diseases like cancer. But manually annotating and quantifying thousands of nuclei is labor-intensive, time-consuming and could result in inaccurate and inconsistent measurements. In this paper, we propose a computer-assisted solution for detection, quantification and analysis of cell nuclei in 3D microscope images. To detect each nucleus, we utilized an advanced machine learning algorithm – Cellpose 3D. Upon detection of nuclei, we automatically estimated the main parameters including size, localization, distance between nuclei, and their clustering. Detected nuclei were classified into various clusters according to their spatial organization. The results obtained from our research show distinct variations in the sizes and distribution and density of nuclei across tissue. Proposed approach minimizes labor, provides accurate measurement and analysis of large 3D image datasets. The method described in this paper could be useful for understanding of tissue organization and the progress of certain diseases.

Mental health and well-being in the workplace and vulnerable communities are paramount concerns for organizations and society. Mental health is directly integrated into the United Nations Sustainable Development Goal 3 (Good Health and Well-Being), which seeks to “ensure healthy lives and promote well-being for all at all ages.” At the workplace, the economic costs of prolonged stress have been estimated in the trillions of dollars globally, with costs potentially reaching 2.9 times those of health insurance and 17.1 times those of employee training (Martinez et al., 2025). Given the significant consequences of stress for workers, organizations, and communities, developing effective approaches to monitor and understand workplace stress and well-being is essential for creating healthier workplaces and communities. Despite the importance of these issues, significant challenges remain, including limited availability of data, the lack of integrated indicators of well-being, and the dynamic and multidimensional nature of well-being. These challenges are particularly important for decision-making in organizational and community settings, where timely and actionable information is needed to guide policies, programs, and practices. Multisectoral collaboration is also essential for strengthening wellbeing monitoring strategies and translating data into meaningful action (Thorpe & Gourevitch, 2022; Thorpe et al., 2022). Our research addresses these challenges through community-engaged and applied research. Specifically, we (1) examine individual, organizational, and community resources associated with mental health and well-being across diverse occupational groups and communities, with a focus on identifying mechanisms that can inform the design and implementation of interventions; and (2) examine innovative approaches to integrating technology and data to measure and monitor well-being and improve decision-making related to policies, programs, and practices. Undergraduate students will be integrated into ongoing research projects and will have opportunities to contribute to activities across the research process, including literature reviews, data collection and management, data analysis, interpretation of findings, and dissemination of research results.

Electric vertical takeoff and landing (eVTOL) aircraft are emerging as a promising technology for the future of aviation. Unlike conventional airplanes, tiltwing eVTOL aircraft can take off and land vertically and then transition to efficient forward flight by changing the orientation of their wings and propellers. During this transition, the wings can experience rapidly changing airflow conditions that differ greatly from those encountered during conventional steady flight. This project will investigate the unsteady aerodynamic behavior of a NACA 63-215 airfoil, a wing cross-sectional shape relevant to ongoing tiltwing eVTOL research. The student will use computational fluid dynamics (CFD) to simulate the airfoil as it undergoes a controlled pitching, or oscillating, motion. The goal is to understand how changes in the speed and magnitude of this motion affect the airflow around the airfoil and the aerodynamic forces it produces. The student will begin by learning fundamental concepts in aerodynamics and fluid mechanics, including lift, drag, flow separation, and stall. They will then receive hands-on training in CFD and learn how to create computational models, generate meshes, run simulations, visualize airflow, and analyze aerodynamic data. Particular attention will be given to observing how vortices form and move over the airfoil as it oscillates and how these unsteady flow structures influence aerodynamic performance. No previous experience with CFD or advanced aerodynamics is required. The project is designed specifically for a first-year student interested in aerospace engineering, fluid dynamics, computational modeling, or emerging aviation technologies. Through the project, the student will gain early exposure to university-level research while developing practical skills in engineering simulation, data analysis, scientific visualization, and technical communication. The results will contribute to a broader understanding of unsteady aerodynamics relevant to next-generation tiltwing eVTOL aircraft and may provide opportunities for the student to continue the research beyond the First-Year Scholars Program.

Construction sites change constantly as materials, tools, equipment, and temporary work areas are added or moved. These changes can create blocked walkways, cluttered work areas, trip hazards, and other access problems. The primary goal of this research is to evaluate whether artificial intelligence can accurately identify selected access and clutter hazards from helmet-mounted 360° construction-site imagery. In this project, the student will help collect 360° video and image data from active construction sites using a helmet-mounted camera. The research will focus on a small set of visible site conditions, such as blocked walkways, debris in access areas, improperly stored materials, and cords or hoses crossing walking paths. Representative frames will be extracted from the 360° recordings and converted into standard image formats, such as JPEG or PNG, for analysis. The student will review selected images and classify site conditions using a simple checklist provided by the faculty mentor. The same images will then be analyzed using existing computer-vision and vision-language AI models. The student will compare the AI results with the manually reviewed images to evaluate how accurately the models identify the selected site conditions. This project is designed for first-year students and does not require prior experience with construction safety, artificial intelligence, or data analysis. The student will receive guidance on site data collection, image preparation, data organization, labeling, basic analysis, and interpretation of results. By the end of the project, the student will contribute to a real construction-site image dataset, evaluate the performance of AI for identifying selected site conditions, and prepare a research poster for the Symposium of Student Scholars. The findings may also support a future conference paper on the use of 360° imagery and AI for construction-site monitoring.

Many older adults do not participate in strength training due perceived risk of injury or low energy. Strength training with lighter loads is a good approach to decrease barriers and potentially improve adherence in this population, however, lighter loads do not improve strength as well. It is possible that imagining maximal muscle effort while lifting lighter loads enhances strength gains and cognitive function older adults. We will recruit older adults aged 65-85 with verified muscle weakness to participate in this study. Video analysis of a chair rise performance will determine muscle weakness. The study will examine changes in muscle strength, muscle size, mobility, and cognitive function in a conventional training group using lighter loads and a group that uses intense mental effort while using lighter loads. We will examine muscle function and size of the biceps and quadriceps muscles. Ultrasound will be used to measure muscle size. Handgrip and quadriceps strength will be assessed. Mobility outcomes will include chair rise performance and walking velocity. Our findings may identify an alternative exercise training strategy that makes it easier for muscle strength gains in older women. This is critically important since older women are more likely to have reduced quality of life as a result of impaired muscle function.

Cardiovascular disease remains one of the leading causes of death in the United States, but its impact is not evenly distributed across communities. Where people live, their access to health care, income, education, housing conditions, transportation, and other social and environmental factors can all influence cardiovascular health. Understanding why some communities experience higher cardiovascular disease mortality than others is an important step toward improving health equity and guiding public health resources. This project will use geographic data and machine learning to investigate how social determinants of health are associated with cardiovascular disease mortality across different places. Rather than focusing only on whether certain factors are related to cardiovascular outcomes, we will also examine whether their effects vary geographically. For example, limited access to health care may be more strongly associated with cardiovascular mortality in some communities than in others. Students will be introduced to geographic information systems (GIS), data visualization, spatial analysis, and machine learning through hands-on research activities. A major focus of the project will be explainable machine learning, which refers to methods that help researchers understand why a computer model makes a particular prediction. These techniques allow us to identify which social and environmental factors may be most influential and how their importance may differ from one location to another. Working with the faculty mentor, students will learn how to organize and explore real-world health and geographic datasets, create maps and visualizations, interpret analytical results, and communicate findings to both scientific and general audiences. No previous experience with GIS, programming, machine learning, or health research is required. Students from geography, public health, data science, computer science, biology, sociology, and other disciplines are encouraged to apply. By the end of the project, students will contribute to research that helps reveal where cardiovascular health disparities are greatest and what community-level factors may be associated with those differences.

The Veterans & Visionaries project started at KSU in 2024 and is focused on telling the life stories of Veterans who served their country during periods surrounding the racial integration of the military, who participated in the Civil Rights Movement, and who are buried in Veterans’ memorial cemeteries in the Southeast. We use an innovative technique to review and integrate cemetery burial records and ancestry records to find potentially eligible participants. By interviewing living family members, we collect the Veteran’s life story and turn the themes of resilience into tools for community outreach and K-12 education. We have developed a suite of in-person and digital outreach and learning tools, including a traveling eight-panel museum exhibit, storyboards, interactive maps, guided cemetery tours, and lesson plans for multiple age groups of learners. This project is interdisciplinary, as it blends expertise and methods from social and behavioral sciences, education, computer science and technology, public history, and art and design. Students working on this project will receive well-rounded training in these interdisciplinary perspectives and exposure to research activities including but not limited to archival records review, research ethics, qualitative (interview) data collection and analysis, community engagement implementation and evaluation, and academic writing and presentation.

Vibration of game paddles such as used in sports involving ping-pong and pickleball has shown to be correlated to the noise generated when a paddle strikes a ball, which leads in some cases to community noise problems.  These paddles relate to the vibration of beams and plates that are used studied in engineering vibrations courses.  The course, ME 4501 Vibrations and Controls Lab, needs to be updated with more complex vibration experiments that relates to real world products.  Building off a successful FYSP work last year, the student selected for this research opportunity will learn to use the Simens software, vibration shaker, sensors, and data acquisition system to collect the data.  A tabletop test rig will need to be designed for accommodating a small beam, plate, and ping-pong paddle will be constructed.  Finally, test data will be acquired and a procedure developed so this can be reproduced by students taking the lab course.

Agriculture is increasingly affected by challenges such as extreme weather, plant diseases, rising production costs, and limited water and labor resources. New technologies can help farmers better understand crop conditions and make timely decisions, but many existing tools are expensive, difficult to use, or require advanced technical knowledge. This project aims to develop a Virtual Personalized Agronomist, an accessible smart-agriculture system that combines sensing, artificial intelligence (AI), and interactive visualization to help growers monitor crops and make more efficient and sustainable management decisions. First-year scholars from engineering, computer science, biology, and related disciplines will work together as an interdisciplinary team. No previous experience with agriculture, AI, or sensing technologies is required. Students will learn the necessary skills through hands-on activities, teamwork, and faculty mentoring. Students will contribute to one or more of three connected research areas under this project:  1. Smart Sensing for Crop Monitoring: Students will help build a multimodal sensing system for a controlled-environment agriculture facility, such as a hydroponic system. Sensors and cameras will be used to collect information about environmental conditions and nutrient solutions, including air temperature/humidity, light, pH, EC, water temperature, and crop images. Students will learn how real-world agricultural data are collected and organized. 2. Edge AI for Crop Health Detection: Students will explore how small AI models can analyze sensor measurements and crop images directly on local computing devices, including Microcontroller, Raspberry Pi, and Nvidia Jetson Nano.  The goal is to identify changes in plant health, growth, or environmental stress as early as possible while reducing the need to continuously send large amounts of data to remote servers.  3. Digital Twin for Agricultural Decision Support: Students will help develop a user-friendly virtual representation of the growing system using AR/XR technology. This digital twin will combine sensor readings, images, and AI results into an interactive interface that allows users to view current crop conditions, identify potential problems, and receive timely management information through LLM-based Chatbot.  Through this project, scholars will gain experience in interdisciplinary research, experimental design, sensors, programming, AI, data visualization, and plant science. Most importantly, students will see how knowledge from different fields can be combined to address real-world challenges in food production and sustainable agriculture.

This research project investigates martial arts performance in Taekwondo using multisensory virtual reality (VR) to better understand acrobatic movements and design immersive user interfaces that enhance training experiences. Although VR enables users to safely experience and practice complex Taekwondo movements, rapid body motions and frequent head rotations can induce cybersickness and reduce user comfort. This project aims to develop a game-based virtual Taekwondo competition that allows users to compete against a virtual opponent while experiencing immersive multisensory feedback designed to improve realism and minimize cybersickness.   The research will utilize a custom-developed VR Taekwondo simulation integrated with haptic technologies, including tactile gloves, a haptic vest, and ankle-mounted vibration devices to provide realistic sensory feedback during martial arts interactions. User data will be collected during these immersive experiences to investigate engagement, perception, physiological responses, emotional experiences, and overall user performance.   The First-Year Scholar (FYS) student will work closely with a graduate student to support this research by assisting with system testing, user studies, data analysis, and the interpretation of cognitive, perceptual, and physiological responses observed during VR-based martial arts training. Through this hands-on research experience, the FYS student will gain valuable experience in immersive system design, human-subject research, virtual reality development, human-computer interaction, and affective computing. The long-term vision of this project is to establish a foundational framework for adaptive, multisensory immersive training systems that can support skill acquisition in physically demanding activities. Such systems have broad applications in higher education, athletic training, military and government training, rehabilitation, and remote skill development.

What makes an athlete faster, stronger, more resilient, or better able to recover? Modern sport science answers those questions by combining human-performance testing with physiology, nutrition, biomechanics, data science, and technology. First-Year Scholars selected for this project will join the Wellstar Center for Human Sport Performance & Wellbeing and become part of an interdisciplinary research team rather than being assigned to only one experiment. Students will rotate through active Center projects and then become more closely involved with one or more areas that match their interests. Current opportunities include research involving high-intensity functional training (including CrossFit), KSU varsity and club-sport athletes, and local youth, high-school, recreational, and professional athletes. Students may also assist faculty collaborators studying: 1) hydration, diet and nutrition, 2) exercise-related blood-flow dynamics, and 3) how athletes produce force and power during exercise. No prior research experience is expected, and students from any major are welcome. Exercise science students will have obvious opportunities, but students interested in data analytics, computer science or software engineering, physics or biomechanics, biology, chemistry, and mechanical engineering can contribute important skills to modern sport science. The most important qualification is a genuine interest in sport or exercise and a willingness to learn. The skills you will learn will often be relevant to the projects in which you participate. Students will be asked to help collect and organize data, operate performance-testing or monitoring equipment, assist with body-composition assessment, work with athlete surveys and nutrition records, learn basic data analysis, or contribute to technology and data-management projects. Students will also learn how research findings can be translated into practical information for athletes and coaches. This experience is intended as the first step in a longer development pathway. First-year students begin by learning to assist safely and reliably under supervision. Students who continue with the Center can progressively assume responsibility for equipment, data collection, analysis, presentations, and eventually leadership of their own research activities. The goal is to develop students who are not only capable research assistants, but are better prepared for graduate school, sport-science careers, strength coaching, and related professional opportunities.

This project explores how organizations create and communicate a culture of information security through everyday experiences, messages, and practices. Information security culture artifacts are the visible and tangible elements of an organization’s security culture, including things employees can see, hear, or experience, such as awareness posters, training materials, recognition programs, security-related communications, workplace reminders, and organizational traditions. Understanding these artifacts is important because they can influence employee attitudes and behaviors, helping organizations promote stronger security practices and reduce human-related security risks. The research team will examine existing academic and professional literature to identify what is currently known about information security culture artifacts. In addition, the project will collect data from information security professionals to better understand the types of artifacts they have encountered in practice and how these artifacts are used within organizations. Using these insights, we will develop a framework for classifying different categories of information security culture artifacts and explore how organizations can adapt these artifacts to support specific security goals and employee behaviors. The ultimate goal of the project is to provide practical guidance that organizations can use to identify existing security culture artifacts, understand their role in shaping employee behavior, and design new artifacts that align with organizational values and security objectives.