Designing for Honest AI Use: What Happens When Students Must Disclose?

Abstract

Asking students to disclose their AI use seems straightforward—until you try it. This lightning talk reflects on efforts to require student disclosure of generative AI use across four graduate-level courses, including three asynchronous online offerings. In response to growing ambiguity around academic integrity, I introduced a combination of explicit syllabus language, ongoing communication, and an AI transparency checklist that asked students to document how AI tools were used across stages of their work (e.g., ideation, drafting, editing). The goal was to move away from a policing model of academic integrity toward a transparency-based approach that treats AI use as permissible but accountable. Students were encouraged to view AI as a partner in their learning process while maintaining responsibility for accuracy, attribution, and original thinking. What emerged was more complex than anticipated. While some students engaged with the transparency framework as intended, others continued to use AI in ways that were undisclosed or inconsistent with course expectations, including cases referred for academic misconduct. These mixed outcomes suggest that requiring disclosure alone does not resolve the challenges faculty face. Instead, transparency appears to surface a deeper issue: many students lack clear frameworks for deciding when and how AI use supports—or undermines—their learning. This session will share practical materials (including a reusable transparency checklist) and reflect on implications for teaching. I argue that disclosure requirements must be paired with intentional efforts to build student judgment and AI literacy, reframing integrity as a function of visible process, not just final products.