Initiatives Cohort

The Interdisciplinary Initiatives Seed Grants Program supports interdisciplinary research teams among KSU鈥檚 researchers in all areas of research, scholarship and creative activity. This one-year support is given for collaborative proposals with the anticipation of a major grant submission upon its conclusion. Seed funding allows KSU鈥檚 researchers to demonstrate the feasibility of their idea and create pilot data to be more competitive for extramural funding.

The Office of Research is currently funding the final round of Initiatives seed grant projects. We will be pivoting to new funding mechanisms in the 2025-2026 academic year.

CCSE C-Day

 

Initiatives Project Index

Modupe

Enhancing Dementia Caregiving with AI-based Dementia Care Voice Assistant Application

Lead PI: Modupe Adewuyi

Team: Modupe Adewuyi, Xinyue Zhang

Project Summary: Approximately 83% of the over 6.5 million persons with Alzheimer's disease and related dementias (Pw-ADRD) in the United States are cared for at home by informal caregivers. This amounts to more than 11 million Americans providing an estimated 18.4 billion hours of unpaid care, which is valued at $346.6 billion in 2023. Behavioral symptoms, such as agitation and wandering, affect approximately 90% of Pw-ADRD, particularly in moderate to severe stages. These symptoms not only impact the quality of life but also increase the likelihood of transitioning to nursing homes, as well as the risk of comorbidities, complications, and caregiver stress.

Non-pharmacological interventions, including music therapy and animal-assisted therapy, have been shown to be effective and are recommended as first-line treatments. However, many informal dementia caregivers lack the knowledge and skills to implement these interventions effectively to manage symptoms.

Our research aims to develop and evaluate a data-driven AI-Dementia Care Voice Assistance (AI-DECAVA) app that provides real-time, personalized guidance to help informal caregivers manage behavioral symptoms associated with dementia. This support is designed to alleviate stress and improve dementia care. Designed for informal dementia caregivers with varying levels of technical skills and English language proficiency, it promotes caregiver autonomy, offers evidence-based assistance for managing dementia-related behavioral symptoms, alerts users to emergencies, and tracks events to facilitate communication with healthcare providers.

Our key achievements include developing the first prototype of the app based on paired dataset-driven interventions, insights from the qualitative phase of the study, and collaboration with our community advisory board. Successful mockup testing resulted in an impressive usability score of 92 out of 100.

Additionally, we have shared preliminary developmental methods and findings through two international conference proceeding papers, one peer-reviewed abstract publication, three oral presentations at international conferences, and one peer-reviewed journal publication. A pilot test is currently underway to assess the feasibility and effectiveness of the AI-DECAVA app prototype, positioning it as a transformative support tool for home-based dementia care.

Ergai

Personalized AI-Powered Task Assistance System for Critically Ill Patients

Lead PI: Awatef Ergai

Team: Awatef Ergai, Sylvia Bhattacharya
 
 Project Summary: To enhance the quality of hospital care and improve healthcare delivery, researchers at 麻豆传媒社区 are designing a personalized, AI-powered task assistance system. The first phase of this project employed a user-centered design approach, engaging directly with patients and healthcare providers to better understand their needs, preferences, and concerns related to developing generative AI tools for healthcare settings. Participants expressed openness to the use of digital technologies in hospital settings, particularly when tools are intuitive, informative, and supported by clear communication. Across perspectives, there was strong emphasis on emotional support, personalized care, and assistance with tasks such as understanding health information, managing medications, streamlining documentation, supporting clinical decision-making, safeguarding privacy, and preserving the human connection in care. The seed grant enabled this foundational work, ensuring the future AI-powered system is designed with users, not just for them. By aligning technology with real-world needs, this research aims to enhance patient outcomes, reduce provider burnout, and foster more compassionate, efficient, and responsive hospital care.
 

Glassmeyer

Supporting the Math Teachers You Wish You Had: A Statewide Pathway for Developing and Retaining Mathematics Teacher Leaders in High-Needs Schools

Lead PI: David Glassmeyer

Team: David Glassmeyer, Kimberly Gardner, Sheryl Croft

Project Summary: This project resulted in an external National Science Foundation (NSF) grant application being successfully submitted for consideration in the 2024 Noyce Track 3: Master Teacher Fellows program. The 5-year, $3 million grant was written in partnership with 10 high-needs school districts from around the state to empower a cohort of 36 secondary mathematics teachers (Fellows) to complete carefully curated online graduate coursework, culminating with Fellows receiving both a specialist degree and a certificate of teacher leadership. The project leveraged KSU鈥檚 existing online graduate degree programs for secondary mathematics teachers, KSU's alumni network, K-12 school partnerships, and connection to Georgia non-profit organizations. In February 2025, the Principal Investigators of this project were informed by NSF program officers about changes to the organization and the national funding landscape and that the grant was not selected for funding.

Gooding

Revolutionizing Clandestine Grave Detection: Integrating AI with GPR for Enhanced Forensic Recovery of Buried Remains

Lead PI: Alice Gooding

Team: Alice Gooding, Da Hu
 
 Project Summary: At 麻豆传媒社区, we are using advanced simulation techniques to improve how ground-penetrating radar (GPR) detects buried human remains. By simulating realistic burial scenarios and analyzing GPR signal responses, our research helps forensic teams better interpret subsurface data and identify potential grave sites with greater accuracy. Supported by the Interdisciplinary Seed Grant, this project has produced valuable insights that enhance training, guide future field applications, and ultimately support faster, more effective investigations. This work demonstrates KSU鈥檚 role in advancing technologies that directly benefit public safety and justice.

Knowlton

Body Brush: Interactive Software for the Creation of Embodied Visual Art

Lead PI: Andrea Knowlton

Team: Andrea Knowlton, Kyungeun Lim

Project Summary: We imagine an interactive software where ephemeral movement creates stunning visual art. By centering human movement in the visual art creative experience, we harness the innate humanity and benefits of embodiment.

 

Kogler

User-Optimized Variable Joint Stiffness Design for Ankle Foot Motion Control in Orthoses and Exoskeletons

Lead PI: Geza Kogler

Team: Geza Kogler, Ayse Tekes, Coskun Tekes

Project Summary: Bioinspired, variable-stiffness joints offer a promising pathway to improve gait performance and user satisfaction by better matching the mechanical characteristics of the orthosis to individual joint behavior. Such designs have the potential to reduce fatigue, restore more natural gait dynamics, and ultimately support greater independence and quality of life for individuals with foot drop. Many of today鈥檚 orthotic and pediatric prosthetic devices are built using rigid materials and standard designs that don鈥檛 adapt to the individual. Therefore, they often feel uncomfortable, don鈥檛 match how joints naturally move, and can lead to poor walking patterns over time. Even though we have advanced tools in robotics and prosthetics, similar innovation hasn鈥檛 reached orthotics, especially not in a way that鈥檚 affordable, lightweight, or easy to customize. Adults with foot drop often face increased risk of falling, reduced walking efficiency, and decreased physical activity due to the mechanical limitations of standard orthotic devices.

The project focused on two aims:

1. Design and development of a compliant and variable stiffness joint to replicate passive ankle stiffness observed during walking, aiming to support more efficient and natural gait in individuals with foot drop.

2. Development of an ankle stiffness measurement test device.

Passive Ankle Foot Orthosis Joints (pFOA). We designed several bio-inspired and compliant passive joints that replicate the nonlinear stiffness behavior of the human ankle, as shown in Figure 2.

Preliminary Compliant AFO Design. Our initial design, as illustrated in Fig. 3, consists of a fully foot plate, a compliant joint, and the shank section that extended along the posterior calf.

Ankle joint stiffness measurement apparatus. We designed an ankle joint stiffness measurement apparatus (as shown in Fig. 4) to conduct 鈥渘ormative鈥 baseline measurements of ankle joint stiffness on healthy subjects with different foot lengths using a custom-built ankle joint stiffness measurement system.The ankle stiffness measurement system uses a computer-controlled custom motorized system that dorsiflexes the foot ankle quantifying ankle joint angle. It also simultaneously measures and records the force (torque) resistance that results during dorsiflexion range. We also developed a custom graphical user-interface based Matlab application which can control the hardware and perform recording of the experimental data as shown in Fig. 5.

Megan Lee

The Impact of Housing Shifts due to Gentrification on Positive Youth Development

Lead PI: Megan Lee

Team: Megan Lee, Chris Hess, Llewellyn Cornelius

Project Summary: This project, led by Dr. Megan Lee and a collaborative team, examines how changes in housing and community structures, such as rising costs and college student-dominated neighborhoods, impact youth's connection to their communities.

By combining personal stories with data, the research provides a historical context of demographic changes within one community and highlights the voices of residents and real challenges families face. The seed grant enabled the team to launch this pilot study, establish community partnerships, and initiate data collection that will inform future solutions.

Ultimately, this work aims to shape policies and programs that support youth in rapidly changing neighborhoods, ensuring that as communities grow, they remain inclusive and supportive for all.

Bo Li

3D Hierarchical Structure via Supramolecular Assembly of Biomolecules for Flexible Energy Storage Materials

Lead PI: Bo Li

Team: Bo Li, Ashish Aphale, Beibei Jiang, Lei Shi

Project Summary: The goal is to leverage directional supramolecular assembly of oligopeptides to build 3D architecture with interconnected conductive network, tunable mechanical properties and hierarchical structures over multiple length scales, for flexible electrode materials towards applications in solar cells and batteries. Active electrode materials with 3D architecture for flexible solar cells and batteries share a common requirement: sufficient pathways for ion/molecule transport as well as conductive network for electrons to exit into external circuits. However, it has proven to be extremely difficult to build such 3D architecture with molecular resolution using conventional 2D lithographic fabrication methods (i.e. 鈥渢op-down鈥 strategy). To this end, crafting 3D architecture via self-assembly of functional nanomaterials using a 鈥渂ottom-up鈥 paradigm provides a promising solution to these technological challenges. Among various nano-building blocks, biomolecules such as DNA and peptide exhibit promising capability as building blocks for constructing 3D architecture with nanoscale resolution, due to their programmable intermolecular bonding. The proposed work will provide a feasible strategy for fabrication of 3D architecture by controlling supramolecular assembly of oligopeptides at multi-length scale. The proposed work will use a combination of experimental and analytical methods to understand the 3D architecture formation process, and, more importantly, how such process determines its electrical and mechanical properties of the formed structures.

Jiho Noh

Enhancing Student Engagement with Peer Questioning in Immersive Virtual Classroom using Large Language Models

Lead PI: Jiho Noh

Team: Jiho Noh, Dabae Lee, Sungchul Jung, Taeyeong Choi

Project Summary: This project, conducted by the YesNLP Lab (led by Dr. Jiho Noh) at KSU, focuses on enhancing student engagement in online/virtual classrooms by utilizing AI/ML technologies, including LLMs, to generate timely and relevant questions. By integrating AI-driven questioning techniques, we aim to improve learning outcomes and teaching effectiveness in asynchronous learning environments. This research is significant as it leverages cutting-edge AI technology to transform traditional pedagogical methods, making the learning experience more interactive and personalized. The KSU SEED grant has enabled us to develop tools that dynamically generate, analyze, and evaluate questions, ultimately fostering a more engaging and practical educational experience for students.

Melissa Osborne

Innovating to Prevent Child Injuries in the Home: Leveraging Immersive Virtual Reality and Real-World Simulation to Improve Parents' Home Safety Behaviors

Lead PI: Melissa Osborne

Team: Melissa Osborne, Lei Zhang, Allison Garefino

Project Summary: Injury is the leading cause of death among children in the U.S., and the home environment is a common location where these injuries occur. While educational programming and information campaigns exist to help parents better understand how to keep their children safe at home, people learn best when they can get hands-on practice. Simply reading information may help, but it doesn鈥檛 always result in behavior change. Immersive virtual reality (IVR) allows individuals to practice skills in a risk-free environment. With support from the KSU Office of Research Interdisciplinary Seed Grant, our team developed an IVR home safety program called ParentSHIELD-VR. In ParentSHIELD-VR, parents enter a virtual home environment, identify child safety hazards and address them, and manage child supervision in the midst of simulated distractions. We heard from real parents to find out what they wanted to learn about home safety and their thoughts about interacting with IVR. We used their input to develop ParentSHIELD-VR and then tested it with another group of parents. From these test sessions, we learned more about what parents think of the program, how it is helping them learn home safety skills, and what we can improve. We are using this data to take our project to the next level for further development and testing. There are few things parents want more than to keep their children safe and secure. Through ParentSHIELD-VR, we are helping to equip parents with not only the knowledge, but the practice and confidence they need to create safe spaces for their children.

Robin Puttock

The Performance of Place: Historical Landscapes, Dance, and Cultural Narratives at Serenbe Art Farm

Lead PI: Robin Puttock

Team: Robin Puttock, Tom Okie, Jacqueline Springfield, Autumn Eckman, McCree O'Kelley

Project Summary: The performance was the result of an interdisciplinary collaboration between KSU faculty and staff who came together to ask two basic questions: How are places made? And how might a contemporary performance evoke and critically explore a particular place? To answer these questions, we drew on the disciplines of landscape studies, contemporary dance, architectural site readings, set design, and place-based history and storytelling. Our hope is that faculty, students and community members who attended the performance will leave with a greater appreciation for the land on which we work and live, the people who have preceded us in this place, and the need to take care of what we have inherited.

Herman Ray

Hope for a Better Future: Building Collaborative Resilience in Liberia (HOPE)

Lead PI: Herman Ray

Team: Herman Ray, Volker Franke

Project Summary: This pilot project uses a new type of artificial intelligence (AI) to look at how fairly resources and opportunities are shared among high schools in Cobb County, Georgia. The goal is to understand whether all students, no matter their background, are being treated equitably.

To do this, we use a tool called a "knowledge graph" and include human oversight to guide the process. The AI helps us organize and analyze information from documents like student handbooks, school improvement plans, staff assignments, and available student services. These documents are often written in different formats, so we use AI to find patterns that might otherwise be hard to spot.

By comparing information across different schools in the same district, we look for differences in how equity policies are written.

Tiffany Roman

SpectrumPlay: Supporting Inclusive Music Literacy of Students in the General Elementary Music Classroom

Lead PI: Tiffany Roman

Team: Tiffany Roman, Rachel Sorenson

Project Summary: Research that matters in the field of education means investigating socially responsible questions in collaboration with practitioners to address serious problems related to teaching, learning, and performance. A learning context that is often overlooked is the elementary music classroom, a place where teachers are expected to show that all students, ranging from gifted to those with special needs, are meeting state music education standards. Student standards include reading rhythm, keeping a beat, singing, and *independently* playing an instrument. This is a huge challenge for music teachers who have limited instructional time, large classes, and lack the bandwidth to provide all students the feedback they need to feel successful.

To address this existing problem of practice, Dr. Tiffany Roman, an Associate Professor in the School of Instructional Technology and Innovation (SITI), and elementary music teacher and SITI doctoral candidate, Erin Collins, partnered together to eliminate the complexities and barriers of music notation for young learners. They co-created a digital tool that personalizes inclusive music learning at scale. The tool they designed, SpectrumPlay, supports independent student learning and play by visually simplifying music notation into scaffolded levels. Intended to be used with a color-coded instrument, SpectrumPlay gives students voice and choice in their learning process. Students are able to remain engaged, have fun, and learn independently.

With the support of the Seed Grant project, Collins and Roman were able to examine SpectrumPlay with intended users (463 elementary students and their teachers) in an authentic context (elementary music classrooms). Regardless of how teachers used the tools in their instruction, following the use of SpectrumPlay, student perceptions were positive across the motivational constructs of empowerment, usefulness, success, interest, and [teacher] care.

At KSU, researchers and practitioners are working to eliminate the barriers and complexity of music notation for learners with special needs. SpectrumPlay gives elementary music teachers, like Erin, an easy-to-use software that supports tailored one-on-one music instruction AT SCALE every single day.

Chris Voicu

WE SHARE: Wheelchair Exoskeleton Synergy for Holistic Adaptive Rehabilitation Evolution

Lead PI: Razvan Voicu

Team: Razvan Voicu, Muhammad Hassan Tanveer, Yannique Tello

Project Summary: The WESHARE (Wheelchair Exoskeleton Synergy for Holistic Adaptive Rehabilitation Evolution) project at 麻豆传媒社区 is pioneering a new standard for how mobility-impaired individuals receive daily rehabilitation. Millions of people around the world rely on wheelchairs, yet traditional rehabilitation methods often require them to travel to clinics and use complex equipment that may not be accessible, affordable, or tailored to their needs. WESHARE aims to change that by transforming the wheelchair into an intelligent, multifunctional rehabilitation platform.

This project combines a lightweight, modular lower-limb exoskeleton with a high-functionality wheelchair frame to support seated users in performing essential physical therapy exercises at home or in care facilities. Whether users are recovering from injury, stroke, or managing a lifelong condition, the system offers continuous therapy through passive, assistive, and resistive training modes. It specifically targets critical muscle groups in the legs and feet to improve blood circulation, prevent muscle atrophy, and maintain joint flexibility鈥攁ll while users remain safely seated. This daily movement is especially important for older adults and rural populations who may face barriers to consistent in-person therapy.

What makes WESHARE unique is its combination of advanced robotics and human-centered design. The system will integrate real-time sensors and intelligent control algorithms to ensure movements are safe, adaptive, and aligned with the user鈥檚 unique biomechanics for human trials. A planned modular mounting system allows the exoskeleton components to attach and detach easily to most wheelchair models, increasing accessibility. Future versions will include integrated health monitoring tools such as heart rate, oxygen level, and blood pressure sensors, along with telemedicine features so healthcare providers can assess patients remotely.

The WESHARE team developed a fully operational prototype, conducted early testing, and presented findings at multiple academic conferences. These efforts have laid the groundwork for expanded research, commercialization, and improved access to rehabilitative care. This work represents KSU鈥檚 commitment to research that matters, enabling greater independence, better health outcomes, and higher quality of life for people with mobility challenges.

Chloe Xie

Accelerating Biomolecular Research by Optimizing Dynamic Simulations using Deep Learning

Lead PI: Chloe Yixin Xie

Team: Chloe Yixin Xie, Bobin Deng

Project Summary: AccelMD is an AI-powered framework designed to speed up molecular dynamics (MD) simulations, which are essential for understanding protein behavior in health and disease. Instead of running full-length simulations, AccelMD uses a small portion of the data to train AI models that accurately predict protein movements, significantly reducing computation time. This approach preserves key physical properties, such as energy trends, and enables faster, more efficient protein analysis for applications in biomedical research and drug discovery.

Zongxing Xie

Passive and Context-aware In-home Fall Detection and Risk Analysis using COTSRadar Sensors with Cross-modal Integration

Lead PI: Zongxing Xie

Team: Zongxing Xie, Chen Zhao, Xinyue Zhang, Mark Geil

Project Summary: Our research team is developing a system that can monitor how people walk (their 鈥済ait鈥) in their everyday environments, without needing them to wear any devices. This type of continuous, contactless monitoring can help detect early signs of health issues such as fall risks, mobility decline, or the onset of neurodegenerative conditions like dementia, as the individuals walk about their own life. By using advanced radar sensors and AI technologies, our project aims to make in-home health monitoring more accessible, affordable, and reliable. This research brings us closer to creating smarter homes that can help families and healthcare providers intervene earlier and more effectively, supporting aging individuals and improving overall quality of life. With support from the seed grant, we successfully developed a working prototype for real-world gait data collection and analysis. We also completed a scoping review paper -- accepted to HumanSys 2025, The Third International Workshop on Human-Centered Sensing, Modeling, and Intelligent Systems Co-located with Cyber-Physical Systems and Internet-of-Things Week 2025 -- that surveys the current landscape of wireless gait sensing research and highlights key opportunities and challenges in the field. In addition, we engaged with technical and health care experts through a survey study to better understand end-users鈥 perspectives on adopting smart sensing technologies in caregiving practices for individuals with Alzheimer鈥檚 disease and related dementias (ADRD). Through this project, we engaged and trained three undergraduate students as research assistants, providing them with hands-on experience in sensing technologies, data analysis, and human-centered research. This opportunity helped prepare them for future careers or graduate studies in computing, healthcare technology, and interdisciplinary research.

Lei Zhang

Feasibility Study of An Immersive Virtual Reality Therapeutic System for Meaning-centered Grief Therapies in Bereaved Parents

Lead PI: Lei Zhang

Team: Lei Zhang, Anisah Bagasra

Project Summary: Bereaved parents who have lost a child to cancer often report a feeling of isolation and a loss of social connectedness after leaving the treatment hospitals. Many of them also struggle to make sense of and find meaning in their traumatic experience of loss due to the untimely death of a young child. Challenges with finding meaning have been associated with prolonged and complicated grief symptoms that can adversely affect grieving parents' quality of life. Although existing videoconferencing and social media tools can be used to provide bereavement support remotely, their user experiences are generally not engaging because of the fourth wall effect (an imaginary barrier that separates an audience from the fictional/virtual world), and they lack custom features to support meaning-centered grief therapies. Leveraging the unique affordances of multi-user immersive virtual reality (IVR) system and text-to-image generative artificial intelligence (AI), we hypothesize that an immersive and individualized therapeutic virtual experience, with support of meaning-centered grief techniques will have great potential fostering social connectedness in bereaved parents in shared virtual worlds and impact their healing journey positively. This seed grant project serves as the preliminary work to the proposed main project with focuses on the following objectives: 1. To assess the user acceptance of the technology proposed by the main project with bereaved parents and grief counsellors. 2. To develop a risk management protocol with grief counsellors and evaluate it. 3. To develop a proof-of-concept multi-user IVR prototype. Outcomes from this seed grant project helped the PI and his team successfully achieved two goals: 1. A conference paper published to Mixed/Augmented Reality for Mental Health (MARMH) at 2024 International Symposium on Mixed and Augmented Reality (ISMAR). 2. Secured an NSF CRII grant award (https://www.nsf.gov/awardsearch/showAward?AWD_ID=2451461&HistoricalAwards=false) for the main project.

Chen Zhao

Non-invasive Fractional Flow Reserve Evaluation using Coronary Computed Tomography Angiography

Lead PI: Chen Zhao

Team: Chen Zhao, Pengcheng Xiao

Project Summary: Researchers at 麻豆传媒社区 are developing a non-invasive, fast, and accurate method to assess the severity of coronary artery disease (CAD)鈥攖he leading cause of death in the United States. By using advanced computer algorithms and coronary CT angiography (CCTA), the team aims to measure blood flow in heart arteries without the need for risky and costly invasive procedures. This research has the potential to transform how doctors diagnose and treat CAD, leading to safer, quicker, and more affordable care. In addition to advancing medical technology, the project engages undergraduate students in hands-on, interdisciplinary research at the intersection of healthcare, imaging, and artificial intelligence.