Addressing Student Non-Compliance in AI Use Declarations: Implications for Academic Integrity and Assessment in Higher Education

Fear, ambiguity, inconsistent enforcement, and peer influence drive students to avoid AI use declarations — even when declaration is mandatory. Gonsalves (2025), a mixed-methods study at King's Business School where 74% of students failed to declare AI use despite it being required on a coursework coversheet, uses the Theory of Planned Behaviour (TPB) to explain non-compliance. Students view declaration as risky self-incrimination rather than a neutral administrative task, and the blurring of authorship by generative AI challenges traditional Academic Integrity norms. Clear, consistent, and trust-based policies are needed to foster ethical AI use.

Key Findings

• Non-compliance is high and strategic. 74% of students failed to declare AI use despite a mandatory declaration on the coursework coversheet. Non-compliance is often a deliberate decision driven by fear of academic repercussions, not ignorance or negligence.
• Four barriers to disclosure: (1) fear of academic penalties, (2) ambiguous guidelines about what counts as "AI use," (3) perceived inconsistency in enforcement across courses/instructors, and (4) peer influence and competition ("everyone is using it"; "ChatGPT is like the fourth man" in group work).
• The declaration felt like self-incrimination. Placing the AI declaration alongside plagiarism statements fostered suspicion; students described it as "admitting to something wrong." Many believe AI use should remain private, like using a calculator.
• TPB helps but needs extension. Attitudes, subjective norms, and perceived behavioural control explain compliance, but GenAI's ethical ambiguity, internalized peer norms, and the philosophical divide over whether AI is a personal tool or a resource requiring disclosure complicate the model.
• Students want better support. They recommended clearer guidelines, a "checklist" for AI declarations, workshops on ethical AI use, consistent enforcement, and a "friendly," trust-based policy rather than suspicion and punishment.

Implications

• Develop clear, consistent AI use policies with detailed examples of acceptable use and how to declare it, tailored to course and student needs.
• Train faculty for consistent communication and enforcement.
• Reframe AI policies toward innovation and ethical responsibility, not just compliance — present AI as a learning-enhancement tool to reduce fear of self-incrimination.
• Build institutional trust — supportive, transparent environments where honest declarations are assessed fairly; introduce AI use in low-stakes formative assessments first to normalize declarations.

Connected Concepts

• AI Use Disclosure
• Academic Integrity
• Assessment
• 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
• Vetter Hidden Cost Disclosure GenAI 2026 — The hidden cost of disclosure

Citation

Gonsalves, C. (2025). Addressing student non-compliance in AI use declarations: Implications for academic integrity and assessment in higher education. Assessment & Evaluation in Higher Education, 50(4), 592–606. https://doi.org/10.1080/02602938.2024.2415654