Binghamton University projects awarded AI Seed Grant funding to advance teaching, research, and public engagement

Eight projects, spanning an array of Binghamton University’s academic subjects, have been awarded AI Seed Grants totaling more than $48,000 to advance teaching, research, and public engagement University-wide.

Projects were funded in spring 2026, and connect faculty and students from across the College of Community and Public Affairs, Decker College of Nursing and Health Sciences, Harpur College of Arts and Sciences, Thomas J. Watson College of Engineering and Applied Science, the English Language Institute, and the Institute for Genocide and Mass Atrocity Prevention. They cover issues surrounding public service, human rights, health, education, music, psychology, engineering, data studies, social work, and computing. 

“Together, these grants represent a strong cross-disciplinary portfolio of faculty-led work using AI to advance teaching, research, and public engagement across the University,” said Vice Provost for Online and Innovative Education James Pitarresi. “They’re helping us learn from practical, discipline-specific experimentation with AI while building connections across the campus. The work reflects both the breadth of faculty interest and the opportunity for Binghamton to support responsible, collaborative innovation.”

The AI Seed Grant program creates structured opportunities to test AI in authentic academic and applied settings. It also emphasizes student participation, responsible exploration, and projects that connect AI with teaching, research, public service, and community needs.

“Artificial intelligence can be a powerful tool for helping researchers to sort through data and power new discoveries in a wide variety of fields,” said University President Anne D’Alleva. “This funding will support work across the entire University and further Binghamton’s reputation as an innovator and a leader in applying AI technology for the public good.”

Here is a closer look at the projects:

Designing for the Human Body: Human Factors Engineering Meets Nutritional Science

Health and wellness studies students partner with engineering students to use generative AI in examining how environments, products, and systems can support nutritional health and behavior. The project connects nutritional science, human factors engineering, and AI-supported design in a collaborative learning experience.

(Wendy Brightsen, Lina Begdache, Safa Elkefi)

Assessing LLMs for Continuing Education for Speech-Language Pathologists

Students compare consumer-grade large language models (LLMs) as tools for evaluating research methods and clinical implications in speech-language pathology literature. The project creates a structured setting in which students can examine both the usefulness and the limitations of LLMs for professional and clinical learning.

(Gregory Hallenbeck; Marisa Mooney; Cassandra Natali)

A Convergence Accelerator for Social Work and Computing through AI

Social work and computer science students collaborate on the design and usability testing of a conversational AI agent related to early recruitment into substance use disorder treatment. The project combines technical development with human-centered design and practice-informed evaluation.

(Mina Lee; Matthew Skojec; Sujoy Sikdar)

Developing Cross-Cultural AI Literacy through Teacher Candidates and International Student Partnerships 

Teacher candidates and international students explore AI tools for English language learning, academic access, and cross-cultural AI literacy. The project emphasizes ethical use, collaborative design, and the perspectives that emerge when students approach AI from different linguistic and cultural contexts.

(Beth Clark-Gareca; Kellie Tompkins)

Harmonizing AI: A Transdisciplinary Micromodule on Music, Sound, Sensing, and Intelligent Systems

Engineering and music or other non-STEM students collaborate on sound, signal analysis, acoustic perception, and generative AI. The short interdisciplinary module will culminate in a demonstration of findings presentation that makes intelligent systems tangible across disciplinary boundaries.

(Zimo Wang; ChingNam Cheng)

Leveraging the Innovation Lab to Drive Transdisciplinary AI Solutions in Teaching, Learning, Community, and Society

Psychology and industrial engineering students participate in an AI-supported design collaboration focused on healthcare or medicine-related challenges. The project uses the campus Innovation Lab for project-based learning that connects social cognition, systems thinking, and practical problem solving.

(Faculty: Melissa Zeynep Ertem; Kenneth J. Kurtz)

Students as Analysts: Using Generative AI to Detect Atrocity Risk in Social Media

Teams use generative AI to support social media analysis, dashboards, and policy briefs connected to atrocity-risk detection. The collaboration brings together human rights and policy expertise with computational methods to explore how AI-assisted analysis might strengthen early warning and prevention work.

(Eric Wiebelhaus-Brahm; Kaicheng Yang)

Using AI Tools to Analyze Local Government Ethical Codes

Students use text mining, machine learning, and large language models to analyze ethics codes across local governments in New York state. The project combines questions tied to public administration with data and systems methods, providing students with experience in AI-supported civic research and comparative policy analysis.

(Komla Dzigbede; Vincent Reitano; Kristen Swedberg; Mina Ostovari)