Responsible AI in Education

Last updated: May 2026

Generative AI is reshaping higher education at a pace that exceeds most faculty’s capacity to evaluate, adapt, and integrate it well. My commitment is to treat AI in education as a faculty development problem and a design problem — not as an institutional mandate or a technology adoption checklist.

Four principles guide my work with AI in instructional design, faculty development, and the scholarship of teaching and learning.

Ethics first. Use cases that touch student data, equity, or assessment require explicit ethical framing — not retrofitted policy. If a practice would not survive a transparent conversation with the students or faculty it affects, I do not adopt it.

Transparency always. Wherever AI contributes to a study, workshop, piece of writing, or course artifact I produce or lead, its role is named openly. Audiences deserve to know what they are seeing.

Faculty trust as a design constraint. Faculty have well-founded reasons to be wary of AI tools introduced from above. New initiatives in my work are co-designed with faculty, not rolled out to them — and faculty have the standing to decline.

Workload sustainability. AI tools should reduce, not amplify, the invisible labor of teaching and educational research. A tool that adds three hours to a faculty member’s week has failed the test, even when it produces something elegant.

These principles show up across my work — in an emerging project on Gen AI in health sciences education at UNT Health, in cross-institutional conversations about AI in instructional design through the EDUCAUSE / Penn State ID2ID network, and in the small, daily decisions about how I use AI in my own scholarship and writing.

This statement evolves as the field does. If you’d like to talk about responsible AI integration in your own context, please reach out.