Research
Higher education is one of the most consequential sites where AI is being integrated into society and one of the least understood. Students, instructors, researchers, and administrators are all using and thinking about AI differently, and the field lacks a clear picture of how adoption is actually unfolding. My research helps paint this picture with multiple brushes. Drawing on qualitative, quantitative, and mixed-methods traditions across education, sociology, and science and technology studies (STS), I investigate how students, faculty, institutions, and researchers internationally conceptualize AI, infuse it into their practices, and — in doing so — restructure the educational systems they constitute.
Research Program
My research agenda operates across four organizational levels: students, instructors, institutions, and nationalities.
Students
My master’s thesis in Computer Science has surveyed over 370 STEM graduate students from six R1 U.S. institutions, constituting one of the largest empirical datasets on this population’s AI habits to date. Findings reveal significant polarization in how STEM graduate students relate to AI — with measurable effects on disciplinary understanding, advising relationships, and perceived legitimacy with peers. See Publications for student-focused work from this project, including an in-preparation study on dependency-based AI adoption patterns targeting Computers & Education: AI.
Instructors
My dissertation in Engineering Education qualitatively studies how engineering higher education instructors conceptualize AI, and how these “mental models” shape their instructional decisions and interactions with students and colleagues. Instructors are a critically under-examined population in AI-in-education discourse: their course policies and departmental cultures propagate far beyond individual classrooms. Work from this strand includes an analysis of how engineering faculty use metaphors to construct AI understanding and a study of when and how faculty permit AI use in their courses.
Institutions
Studying individual students and instructors is necessary but insufficient — institutional structures shape what is possible. I actively collaborate with AI governance leads at Virginia Tech, Cornell University, and in New York State K-12 education. In March 2026, I initiated a cross-institutional workshop at Virginia Tech bringing together graduate instructors, educational technologists, and academic integrity organizations around effective AI course policy. Earlier work with Cornell’s discipline-based education research community explored how LLMs are reshaping qualitative research practice.
International
AI adoption is not only a national question. Through an ongoing Fulbright Brazil collaboration, I study and support AI governance coordination at transnational scales — working with institutional leaders making decisions that impact real students. I participated in multi-day workshops in São Paulo in September 2025 and am continuing that collaboration ahead of a Washington DC convening in May 2026. See the Fulbright Brazil workshop for details.
Methods Contributions
Alongside these research settings, I contribute to discourse about how researchers themselves use AI — and what responsible AI-infused methodology looks like.
My published article in the International Journal of Qualitative Methods used LLMs to analyze qualitative educational data, interrogating LLM-infused workflows as a methodological intervention compared to human-based analysis. This work asks how LLMs afford and constrain research processes, and what constitutes “meaningful” and “trustworthy” findings when AI is part of the analytic chain.
An in-review book chapter for the 2027 Cambridge International Handbook on Engineering Education Methods provides a conceptual landscape for researchers navigating these systems, and a companion piece under review at Studies in Engineering Education reframes AI use in research as inherently interpretive work requiring appropriate technical and reflexive practices. A third in-progress article analyzes researcher conceptualizations of AI through conference publications — finding an overwhelming treatment of AI systems as instrumental “tools” rather than as transformational sociotechnical systems. See Publications for the full list.
Governance & Outreach
My research connects me with central decision-makers overseeing AI adoption in STEM higher education. At the K-12 level, I engage with the New York State School Boards Association toward governance systems that protect students against AI harms while supporting responsible exploration across school districts. My collaborations at Virginia Tech, Cornell, and abroad have sharpened strategies for spanning disciplinary and institutional boundaries.
One such strategy requires “AI translation” to bridge different vocabularies across technical and educational communities. A 2026 ASEE conference paper documents the wide-ranging language engineering instructors use to describe AI: from “algorithms” to “assistants” to “tsunamis.” My prior industry experience as a software engineer at two Fortune 100 companies and my M.S. in Computer Science provide a direct link with practitioners driving AI change and demand.
Browse my Talks and Teaching pages for invited presentations and workshops across these governance and outreach efforts.
Vision
Higher education is where the next generations of AI researchers, engineers, and academics are trained, so getting AI adoption “right” here matters enormously. My work is fundamentally about occupying and fortifying the bridge between communities that design AI systems, those that use them, and those that study these human-AI relationships. I see my research contributing frameworks that develop competency, trust, and expertise around AI practice in STEM higher education, particularly for graduate students, instructors, and researchers navigating this critical transition.