Beyond Detection: How Students Use—and Hide—AI in Online Assessments and What Authentic Tasks Can Do About It

Students navigate institutional AI ambiguity with caution and pragmatism — disclosure is rare, driven more by fear of penalties than by dishonesty. Kirsanov, Kushwah, and Selvaretnam (2025/2026), a small case study of undergraduate economics students at the University of Glasgow (31/174 respondents), find that only about one-third report using AI in online assessments, and disclosure is rarer still. Non-disclosure appears to be rational caution in the face of ambiguous policies and perceived academic risk, not simple integrity violation. Students support guidance and structured Regulation, favor citation rules, and widely see real-world, data-based tasks as reducing AI misuse — pointing toward authentic assessment and clear expectations over detection and deterrence.

Key Findings

• Reported AI use is moderate, and disclosure is rare. Only ~34% of respondents acknowledged AI use; most uses were supportive (proofreading, rewording, brainstorming), and no student reported submitting a full AI-generated answer. Many opted out of reporting AI use early in the survey — interpreted as strategic self-presentation rather than proof of non-use.
• Fear of penalties is the dominant barrier to disclosure. About three-quarters of respondents worried about being penalized for AI use, even when use was moderate or constructive ("penalty anxiety"). This is consistent with broader UK/international survey evidence.
• Ethical anxiety predicts lower disclosure. Logistic regression found students with greater ethical anxiety (fear of penalties, unfair access, undermining learning) were less likely to disclose AI use. Notably, UK nationals disclosed significantly more than international students.
• Students want guidance, not bans. Most agreed AI is acceptable when used supportively, and many welcomed clearer citation rules and institutional guidance. Views on a blanket ban were divided.
• Authentic, data-rich tasks are the promising lever. Students widely supported real-world, data-based tasks as reducing over-reliance on AI — by design rather than deterrence.

Implications

• Align assessment with authentic tasks and clear expectations rather than relying on detection and deterrence.
• Address the affective barrier: students' fear of penalty suppresses disclosure even of legitimate, supportive use — policies must signal that honest disclosure will not be punished.
• Provide clear citation and use guidance so students have the framework and confidence to disclose voluntarily.
• Consider equity: international students disclosed less, raising fairness concerns about access to and understanding of AI tools.

Connected Concepts

• AI Use Disclosure
• Academic Integrity
• Assessment
• Authentic Assessment
• Higher Ed
• Generative AI
• Equity In AI Education

Connected Articles

• Gonsalves Student Non Compliance AI Declarations 2025 — Student non-compliance with AI use declarations
• Chang Should I Tell My Teacher AI Disclosure 2026 — Student AI disclosure, stigma, and self-regulated learning
• Vetter Hidden Cost Disclosure GenAI 2026 — The hidden cost of disclosure

Citation

Kirsanov, O., Kushwah, L., & Selvaretnam, G. (2026). Beyond detection: How students use—and hide—AI in online assessments and what authentic tasks can do about it. Journal of Academic Ethics, 24, 14. https://doi.org/10.1007/s10805-025-09691-3