Summer Team Impact Projects

The Office of Undergraduate Education is calling for proposals for Summer Team Impact Project Grants (STIP) for Summer 2026. These projects will support multidisciplinary summer teams of undergraduate students, graduate students, and faculty and staff members, who will explore a faculty-driven central theme, research question, or community-identified problem.

We are particularly interested in proposals that speak to one of the areas of the Grand Challenge Initiative. However, all proposals are welcome. The deadline for submissions is November 21, 2025. These selected projects will begin at the end of May 2026.

Submit a Summer Team Impact Proposal on a question, challenge, or topic, of personal and professional interest. See the submission guidelines and the narrative outline for all the materials that are required as part of the submission.

Questions: email impactgm@gmu.edu

To submit your proposal, you will need the following information: 

1. Your contact information and the information of any faculty or staff members of your team.

2. A project summary (350 words).

3. A Project Narrative (see details below).

4. Student Recruitment Plan

• A position description for undergraduate student researchers, which will be posted on Handshake
• A separate one page document that outlines your student recruitment plan.

5. Project Budget  

• Mentor and Graduate Student Stipends - Each grant can support up to 2 Faculty and staff mentors with a stipend of $3,000 each. In addition, each grant can receive one of the following:
  • Additional faculty mentor with a $3,000 stipend.
  • Graduate student with a $6,500 stipend.

• Undergraduate Students: $6,000 each for each undergraduate student (6-10 students per grant). Please provide the number of expected students.
• Supplies: up to $4,000. Please list items and approximate costs

In a single PDF file, upload a narrative—of no more than four pages—that addresses all the following:

• Overview of the central question or problem and the project that students will be participating in over the summer
• How your project is connected with this year's theme (not required).
• Timeline for the 10-week program including:
  • A first-week student boot camp
  • Enrichment activities for the participating students
• Undergraduate Participation
  • Describe how the undergraduate students will be participating in answering the central question or problem.
  • Describe what you think the undergraduate students will learn and/or take away from their summer experience working with you.
  • Describe expected student interest in the project.
• Involvement of partners
  • Tell us how you are connecting with campus partners, such as the library and research centers
  • If applicable, what off-campus partners are you engaging to answer this question. A letter of support from outside partners participating would be a benefit to the proposal if applicable.

Jenna Krall, MB Mitcham (College of Public Health)

Homelessness has been increasing in the US for almost a decade, reaching its largest count to date in 2024. Beacon Landing, the largest permanent supportive housing project in northern Virginia, is anticipated to open in the City of Fairfax in late 2026. Such projects are critical to provide housing and support the health and wellbeing of future residents. Furthermore, as environmental hazards including extreme heat and poor air quality are expected to increase, projects like Beacon Landing are increasingly imperative. Our previous work with the Lamb Center, a daytime drop-in shelter in Fairfax City, has assessed perceptions of heat and other environmental hazards among those experiencing homelessness. This Summer Team Impact Project will build upon our existing partnership with the Lamb Center to assess perceptions and impact of Beacon Landing. We will work with a multidisciplinary team of undergraduate students to understand the dual challenges of homelessness and environmental hazards, spanning community health, environmental science, and policy. Our aims are to (1) determine perceptions of Beacon Landing under increasing heat and poor air quality among (a.) guests and staff at the Lamb Center and (b.) the City of Fairfax community more broadly; (2) quantify the impact of supportive housing under extreme heat and poor air quality; and (3) examine collaborative governance structures that make projects like Beacon Landing successful. Additionally, we will incorporate an exploration of generative artificial intelligence (GenAI) use into our project. Although undergraduate students may utilize GenAI in courses to supplement their learning, they may not fully comprehend the risks and limitations of GenAI use in research. Importantly, our project has policy implications for vulnerable community members, so GenAI should be used with extreme caution. To address this, students, faculty, and community partners will engage in discussions of ethical GenAI use in the context of our aims throughout this project. This research is highly time critical: Beacon Landing will open in late 2026 and it is imperative to understand perceptions and benefits of this supportive housing project. Future work will assess Beacon Landing's impact following its opening.

Padmanabhan Seshaiyer (College of Science), Holly Matto (College of Public Health) 

Relapse prevention for behavioral and mental health conditions, including substance-use disorders, depression, and PTSD, remains a persistent public-health challenge. Existing interventions are often reactive rather than adaptive, offering limited support during moments of emotional vulnerability. Building on U.S. Patent Application 20190320964: Systems and Methods for Biobehavioral-Regulation Treatments, this project develops a prototype digital-therapeutic system that integrates mobile and virtual-reality (VR) platforms with mathematical modeling to deliver real-time, personalized interventions.

The mobile system monitors biobehavioral signals, detects heightened relapse risk, and provides adaptive recovery cues such as images, videos, and interactive brain-modulation exercises. Mathematical models analyze dynamic behavioral and physiological inputs to predict relapse-risk trajectories and guide the timing, type, and intensity of interventions. A complementary VR platform enables users to design a personalized self-avatar that supports emotional regulation, mirrors calming behaviors, and delivers context-specific coping strategies. By merging mobile sensing, VR immersion, and computational modeling, the system offers a human-centered, adaptive approach to relapse prevention that emphasizes self-regulation, empowerment, and accessibility.

This 10-week summer program will engage six undergraduate and one graduate student in the full cycle of design, development, and testing. Students will apply design-thinking methods, including empathy mapping, problem definition, ideation, prototyping, and evaluation, while learning computational tools such as Unity for VR, mobile-app development frameworks, Python, data-science techniques, and mathematical modeling of behavioral systems. They will also strengthen skills in collaboration, project management, and lifelong learning, preparing them to become interdisciplinary contributors to digital health innovation.

The project aligns with GMU’s Grand Challenge Initiative, addressing Healthy People + Healthy Communities and Digital Innovation for Social Impact. It also supports UN SDG 3 (Good Health and Well-Being), 4 (Quality Education), 9 (Industry, Innovation, and Infrastructure), and 10 (Reduced Inequalities). By providing scalable, evidence-informed tools for emotional regulation and relapse prevention, this work advances equitable mental-health solutions while cultivating the next generation of technology- and health-focused innovators.

Through this project, students will produce an integrated prototype and gain hands-on experience in software development, VR design, adaptive-intervention strategies, human-centered research, and computational modeling. The resulting system and student expertise will form a foundation for broader research, implementation, and sustainable innovation in behavioral health.

Brittany  Hupp (College of Science) 

The Chesapeake Bay and its tributaries compose a dynamic estuarine environment that has economic and cultural significance to communities living on the central Atlantic coast. However, estuarine ecosystems in this region are experiencing rapid environmental change due to both anthropogenic and climate induced stressors. Salinity gradients are rapidly fluctuating due to saltwater intrusion associated with sea level rise and an increase in extreme precipitation events bringing freshwater into the Bay. Seasonal hypoxia (i.e., low oxygen conditions) is also intensifying, creating more extensive dead zones whereby organisms who rely on oxygen die each summer. While these negative impacts are already affecting organisms living in the Bay, it is not clear how these critical estuarine ecosystems will respond to even greater environmental change projected for the not-too-distant future. Therefore, this project aims to investigate how changes in salinity and dissolved oxygen (DO) in estuarine environments impact organisms living in the water column (i.e., fish) and upon the seafloor (i.e., benthic foraminifera) to further inform adaptation and remediation efforts in these critically important environments. To pursue this primary objective, the team will 1) explore Chesapeake Bay microfossil records to reconstruct environmental change and ecological response to past changes in salinity and DO, 2) investigate modern communities of fish and benthic foraminifera living along the salinity and DO gradients found in three major tributaries of the Bay (i.e., the Potomac, Patuxent, and Rappahannock Rivers), and 3) conduct laboratory culturing experiments using live organisms to investigate how differing salinities impact growth. The proposed work will attract students in fields such as environmental science, biology, geology, and chemistry, among others, to tackle research geared toward building climate-resilient ecosystems and societies that depend on them, a focus of GMU’s Grand Challenges Initiative. Students recruited to this project will gain experience conducting field work, culturing experiments, data analysis and interpretation, and laboratory tasks including the use of instrumentation and resources at GMU’s Potomac Science Center. Results from these projects will be presented by undergraduate researchers in a research symposium and contribute to graduate student research, ultimately resulting in peer reviewed papers with all participating team members as coauthors.

Susan Lawrence, Courtney Massie (College of Humanities and Social Sciences, Writing Center)

While stances toward generative artificial intelligence vary among writing educators, a consensus is forming that university writing educators, including writing centers, have a key role in developing students’ critical literacies around AI and writing (AWAC Working Group, 2025; MLA-CCCC Task Force, 2024).

Research on college students’ use of generative AI for writing has addressed the percentage of students using AI for their writing assignments (Essid & Cummins, 2025; Lindberg 2025) and students’ attitudes about using AI for writing (Zieve-Cohen et al., 2025). Researchers have also produced high-level descriptions of the aspects of writing students use AI to support (Essid & Cummins, 2025; Hwang et al., 2025; Kim et al. 2024; Lindberg, 2025; Wang, 2024). For example, we know that students use AI to brainstorm ideas, create outlines and drafts, and edit prose for specific audiences. As of now, however, we have few rich, detailed descriptions of how students are taking up generative AI in their writing processes.

Nor do we know what knowledge of AI students bring to their use of this tool, or the literacies they see themselves developing (or needing to develop) as they use it. Scholars are developing frameworks articulating the literacies writers should develop to use AI effectively and ethically (e.g., AWAC Statement; "A framework”, 2025; Vee, 2025), but few studies have focused on the knowledge writers are (or are not) drawing on as they make decisions about how to use AI in their writing.

To design assignments, curricula, and resources to support student agency and intentionality in effective and ethical AI use, writing educators would benefit from a stronger sense of the landscape of that use.

In this project, the research team will explore how Mason students are integrating generative AI into their writing and writing processes, as well as the knowledge they bring to the task. The team will recruit student participants, interview participants, analyze data, and present at the Celebration of Student Research and Impact in August and the International Writing Centers Association conference in October.

Wenying Ji (College of  Engineering and Computing), Fengxiu Zhang (Schar)

Urban infrastructure systems operate effectively only when people use, interpret, and adapt to them, making the human-in-the-loop layer essential for designing systems that are reliable, equitable, and responsive to community needs. However, the National Academies identify two persistent challenges in modeling this layer: behavioral stochasticity and persistent data scarcity. behavioral stochasticity arises because human perception, decision-making, and adaptation depend on individual dispositions, lived experiences, and situational contexts, producing wide variation in responses across users and conditions. Data scarcity further limits our understanding, as real-world behavioral observations are often sparse, incomplete, and unevenly distributed, making it difficult to infer system-level patterns or maintain behavior models that keep pace with changing conditions.

Large language models (LLMs) offer a promising pathway to overcome these challenges. LLMs can represent latent internal states, produce context-dependent decisions, and update behavioral representations as new information becomes available. They can also synthesize limited or fragmented behavioral data into coherent priors that improve over time. Embedding LLM-based agents into digital-twin environments enables a more adaptive, human-centered modeling framework that captures the complex interactions among communities and infrastructure systems.

This project will establish an AI-empowered human–infrastructure research cohort for undergraduates to advance this emerging frontier. Supported by an interdisciplinary faculty mentoring team, two active NSF projects, and a strong community partnership with Alexandria’s DASH transit agency, the project will provide students with a well-resourced and authentic environment to investigate human–infrastructure interactions. Students will: (1) co-design behaviorally rich LLM-based agents that represent diverse users’ adaptive responses; (2) collect and organize real-world human–infrastructure interaction data within a concrete case setting; and (3) integrate intelligent agents into a preliminary human–infrastructure simulation to explore how alternative designs affect system serviceability.

By advancing LLM-enabled, human-centered models of human–infrastructure interactions, this project supports more adaptive, effective, and reliable decision-making for vehicle-electrification infrastructure planning. In doing so, it directly aligns with Mason’s Grand Solutions—particularly advancing responsible digital innovation, promoting sustainable infrastructure, and improving human health, well-being, and preparedness.