The Hidden Cost of Disclosure: A Multi-institutional Study on Undergraduate Students' Generative AI Usage and Faculty Accusations

Transparency about GenAI use carries a hidden cost: students who disclose may be more likely to be accused. Vetter et al. (2026), a multi-institutional survey of 560 undergraduates across four Northeastern U.S. institutions, find that nearly two-thirds of students never or rarely disclose GenAI use to instructors, and that punitive or ambiguous course policies encourage concealment. Strikingly, students who "always" disclosed their AI use had over three times the odds of being accused by their instructors — an ironic pattern where greater transparency correlates with greater suspicion, revealing a fragile trust relationship and the hidden costs students incur for being honest.

Key Findings

• Most students do not disclose. 66% of GenAI-using students never or rarely disclosed their use to instructors; only ~10% always did. Frequent (daily/weekly) users were significantly more likely to rarely disclose and less likely to always disclose.
• Punitive and unclear policies drive concealment. Students were more likely to never disclose when they were "not aware of course policies" or when AI was "not allowed." A "limited use in certain situations" policy was the strongest positive predictor of disclosure. Nearly 30% were unaware of their courses' AI policies or had policies unaddressed in the syllabus.
• Transparency is risky. Students who always disclosed had over 3× the odds of being accused. Moderate users were most likely to be wrongly accused; heavy and always-disclosing students drew suspicion. Fear of academic penalty was the most common reason for non-disclosure.
• Students want clarity and collaboration. Most students want clear, unified policies that allow GenAI for brainstorming/research/studying, and support collaborative policy development between faculty and students.
• Trust is fragile. While 92% of students viewed AI as at least somewhat positive for learning, the fear of retribution and inconsistent policies chill honest disclosure.

Implications

• Create clear, consistent, transparent AI policies developed collaboratively with students — punitive or vague policies motivate concealment rather than openness.
• Train faculty to build trust, not rely on detection. Faculty should learn trust-building conversations rather than leaning on unreliable AI detection tools; disclosure should never be followed by accusation.
• Reframe disclosure as collaborative, not confession — invite students to articulate how and why they used GenAI, positioning it as part of learning rather than misconduct.
• Watch for equity and irony: students who are most transparent may be disproportionately scrutinized, undermining the very trust disclosure is meant to build.

Connected Concepts

• AI Use Disclosure
• Trust
• Trust Calibration
• Academic Integrity
• Educational Policy AI
• Higher Ed
• Generative AI
• AI Literacy

Connected Articles

• Kirsanov Beyond Detection AI Online Assessments 2026 — Beyond detection: how students use and hide AI
• Chang Should I Tell My Teacher AI Disclosure 2026 — Student AI disclosure, stigma, and self-regulated learning
• Gonsalves Student Non Compliance AI Declarations 2025 — Student non-compliance with AI use declarations

Citation

Vetter, M. A., Farag, I., Jiang, J., Lucia, B., & Silvestro, J. J. (2026). The hidden cost of disclosure: A multi-institutional study on undergraduate students' generative AI usage and faculty accusations. SSRN. https://doi.org/10.2139/ssrn.5755762