Participatory, not Punitive: Student-Driven AI Policy Recommendations in a Design Classroom

TL;DR

This paper explores a participatory approach to AI policy design in education, emphasizing student involvement to create more inclusive and relevant guidelines.

Contribution

It introduces a student-driven workshop model for AI policy development, highlighting benefits of participatory governance in academic settings.

Findings

Students identified concerns overlooked by top-down policies.

Collaborative policy creation led to practical, campus-wide recommendations.

Engagement fostered a sense of ownership and understanding among students.

Abstract

Generative AI is reshaping education, yet most university AI policies are written without students and focus on penalizing misuse. This top-down approach sidelines those most affected from decisions that shape their everyday learning, resulting in confusion and fear about acceptable use. We examine how participatory, student-driven AI policy design can address this disconnect. We report on a three-part workshop series in a graduate design course at a minority-serving university in the U.S., where two student leaders facilitated discussions without faculty present. Eight participants shared candid accounts of their AI use, co-authored ten policy recommendations, and visualized them in a zine that circulated across campus. The resulting policies surfaced concerns absent from top-down governance, such as the double standard of requiring students to disclose or abstain from AI use while faculty face no such expectations. We argue that engaging students in AI governance carries value beyond the resulting policies, and offer transferable strategies for fostering participation across disciplines -- a model for calling students in rather than calling students