A Guided Inquiry Approach to Students Co-Designing Generative AI Course Policies

Synthesis: Hingle and Johri (2026) report an exploratory study in which students co-designed a Generative AI course policy through a guided inquiry activity. Students first drafted individual proposals for appropriate and ethical GenAI use, then refined them collaboratively by incorporating diverse stakeholder perspectives. Analysis of student artifacts and group discussions showed participants prioritized training for students and instructors, standardized disclosure procedures, stronger institutional support, and greater involvement in GenAI-related decision-making. The study offers pedagogical insights into how policy co-design can surface student values and sensemaking about governing AI use in educational contexts.

Co-Designing GenAI Policy Through Guided Inquiry

Institutions and instructors increasingly confront questions about how to support learning while addressing the ethical and institutional concerns raised by Generative AI. Yet students — the people who make most day-to-day decisions about AI use — are largely absent from the policy-making process. This exploratory study examines a guided inquiry activity in which students took an active role in designing a GenAI course policy.

The activity progressed through several stages: students first completed readings on AI use, then used GenAI to brainstorm initial policy ideas; next, they articulated their own perspectives in a written assignment and an individually designed course policy; finally, they collaborated with peers to incorporate diverse stakeholder perspectives into a collective policy. Two research questions guided the study: what practical factors students prioritize in their GenAI use policies and how they justify these choices, and how participants reflect on the policy design process.

What Students Prioritize

1. Training for students and instructors. Participants emphasized the need for clear guidance and skill development around appropriate GenAI use — a demand that maps to AI Literacy and Professional Development.
2. Standardized disclosure procedures. Students wanted consistent, transparent expectations for when and how to disclose AI use in academic work.
3. Stronger institutional support. Rather than relying on individual instructors, students called for coherent, institution-level backing for GenAI policy.
4. Greater involvement in decision-making. Participants wanted a meaningful role in shaping the rules that govern their learning, reflecting a desire for Learner Agency in AI governance.

Students described the policy design process itself as valuable, noting that it forced them to engage with multiple perspectives and the inherent trade-offs involved in governing AI use. This positions policy co-design as both an outcome and a learning activity: it surfaces student values and concerns while developing their capacity to reason about responsible AI use.

What this means for practice

• Instructors. Run the guided inquiry sequence — readings on AI use, GenAI brainstorming, an individual course-policy draft, then a peer group discussion — as a low-cost way to translate student voice into a course-level policy while building students' ethical reasoning about AI.
• Instructors. Provide task-related GenAI training for students and for instructors, since participants described scenarios in which instructors acted on a flawed understanding of GenAI.
• Curriculum designers. Standardize disclosure and citation procedures across courses so students can understand what is and is not allowed without re-learning a different regime in each class.
• Administrators. Build institution-level support rather than leaving GenAI policy to individual instructors: students in this study called for an approved list of licensed tools, privacy safeguards for course data, and affordable or subsidized access.
• Administrators. Give students a genuine role in shaping the rules that govern their learning, since the activity showed students could reason about trade-offs rather than merely react to policy handed down.

Limitations

• Data came from 55 of the 68 undergraduates enrolled in a single course, "Technology and the Global Society," for information technology and cybersecurity majors at one institution, and the authors state the findings are context-specific.
• The study included no baseline or pre-intervention measure and is not intended to demonstrate changes in students' AI literacy or learning gains attributable to the activity; the reported themes may reflect pre-existing views.
• The module was designed around the researchers' prior knowledge of this group's interest in discussing GenAI and their disciplinary background, so replication elsewhere would require similar attention to participant characteristics, rapport, and local relevance.
• The evidence is student-produced artifacts and reflective writing about their own reasoning, so it captures what participants articulated in the activity rather than observed changes in their later AI practice.

Citation

Hingle, A., & Johri, A. (2026). A Guided Inquiry Approach to Students Co-Designing Generative AI Course Policies. arXiv:2608.28501.