"Should I Tell My Teacher?" Student AI Disclosure Practices, Stigma, and Self-Regulated Learning in Higher Education

Student AI disclosure is not only a matter of policy compliance — it is an affective process intertwined with self-regulated learning. Chang, Lin, Huang, and Ryoo (2026), studying 78 undergraduates, find that students' worries about judgment, stigma, dependence, and replacement are associated with greater concealment and peer-only disclosure, while teacher-directed disclosure is consistent with adaptive Help Seeking. A key finding: anxiety does not suppress disclosure wholesale — it redirects it toward safer peer outlets, cutting students off from the instructor feedback that could calibrate their AI use. Disclosure norms vary by discipline and language, and do not develop automatically with academic progression.

Key Findings

• Worry redirects disclosure toward peers, not outright suppression. Higher worry was associated with a desire for total non-disclosure and peer-only sharing, but not with lower disclosure to teachers directly. Anxious students did not stop talking about AI — they moved the conversation to peers, who cannot give the calibrated feedback teachers can.
• Two interpretive response patterns emerge: a transparent tendency (disclosing to teachers, checking with instructors, treating instructor interaction as an external regulatory checkpoint) and an anxious-concealing tendency (frequent AI use for substantive tasks paired with elevated worry and withdrawal from formal feedback — consistent with maladaptive Regulation).
• Heavy AI users are both most worried and least connected to feedback. Students who frequently uploaded drafts for AI review reported the highest worry, more secrecy, and no increase in teacher disclosure — the group at greatest risk of passive, uncritical reliance, and least likely to receive instructional attention.
• Discipline is the strongest predictor of disclosure to teachers. Education, Social Sciences, and STEM/Health students reported higher disclosure than Business students (with Humanities not differing from Business). Language status was marginal (monolingual students tended to disclose less); years of study did not predict disclosure.
• Disclosure ≠ help-seeking, but can make it visible. Help-seeking is obtaining assistance; disclosure makes that assistance visible to others. The two are logically and temporally separable.

Implications

• Behavior-only disclosure policies (mandatory declarations) are insufficient if they don't address the affective barriers — judgment, stigma, fear of penalty — that drive concealment.
• Normalize AI use and reduce perceived judgment through classroom practices, instructor-led examples, and reassurance that disclosure will not be penalized, to lower the social cost of transparency.
• Provide low-barrier disclosure mechanisms — in-assignment reflection prompts, opt-in consultation, embedded AI-use reflection tools — so disclosure feels like learning regulation, not confession.
• Cultivate disclosure norms deliberately; students do not "grow into" transparency with academic progression, so scaffold it from the first year.

Connected Concepts

• AI Use Disclosure
• Self Regulated Learning
• Help Seeking
• Academic Integrity
• Higher Ed
• Generative AI
• AI Literacy
• Equity In AI Education

Connected Articles

• Kirsanov Beyond Detection AI Online Assessments 2026 — Beyond detection: how students use and hide AI
• Gonsalves Student Non Compliance AI Declarations 2025 — Student non-compliance with AI use declarations
• Vetter Hidden Cost Disclosure GenAI 2026 — The hidden cost of disclosure

Citation

Chang, D. H., Lin, M. P. C., Huang, J.-Y., & Ryoo, J. (2026). "Should I tell my teacher?" Student AI disclosure practices, stigma, and self-regulated learning in higher education. Frontiers in Education, 11, 1826174. https://doi.org/10.3389/feduc.2026.1826174