To disclose or not to disclose: Peer influence and psychological factors in students' use of generative artificial intelligence

To disclose or not to disclose — a mixed-methods study of 409 Singaporean undergraduates examining why students conceal their Generative AI use despite institutional disclosure mandates. Guided by Bandura's Social Cognitive Theory, it finds disclosure is primarily predicted by relational and social variables — perceived peer disclosure and comfort with instructors were the strongest predictors, while moral disengagement had weaker effects. Non-disclosure reflects strategic adaptation to perceived peer norms and low interpretive trust in instructors, not moral negligence.

Qu & Wang (2026) reframe GenAI non-disclosure in higher education as a relationally embedded practice rather than simple cheating. Despite mandates, students frequently conceal their GenAI use, reflecting ethical uncertainty and relational risk. The study moves the focus from compliance toward the social environments that make transparency possible.

Method

• Sample: 409 undergraduates at a Singaporean university who reported using GenAI tools for academic purposes.
• Theory: Bandura's Social Cognitive Theory — how cognitive, social, and emotional factors jointly shape disclosure willingness.
• Design: Mixed-methods. Quantitative hierarchical regression tested the effects of moral disengagement, perceived peer disclosure, and relational comfort with peers/instructors on self-reported disclosure likelihood. Qualitative thematic analysis of open-text responses explored students' interpersonal reasoning and perceived risks.

Key Findings

• Relational and social predictors dominate. Perceived peer disclosure and comfort with instructors were the strongest predictors of disclosure likelihood; moral disengagement showed weaker effects.
• Non-disclosure is strategic, not negligent. Qualitative findings revealed non-disclosure reflected strategic adaptation to perceived peer norms and low interpretive trust in instructors, rather than a lack of moral concern.
• Transparency depends on trust. Disclosure is a relationally embedded practice where ethical decisions are co-constructed within specific social and institutional contexts. Non-disclosure may reflect moral adaptation in environments with normative ambiguity and interpretive uncertainty.
• Institutional implication: Transparency depends less on compliance and more on environments that enhance trust and positive normative climates.

Implications

• For Academic Integrity policy: mandates alone are insufficient. Institutions should build trust and constructive peer norms rather than rely on detection or punishment.
• For AI Anxiety And Stress: the "to disclose or not" dilemma — weighing honesty against relational risk and fear of misinterpretation — is a genuine source of student stress around AI use, linking integrity anxiety to social/peer pressure.
• For educators: comfort with instructors and perceived peer behavior shape whether students are willing to be transparent about AI use; relational climate is a designable variable.

Connected Concepts

• Academic Integrity
• Generative AI
• Higher Ed
• Trust
• Ethics
• Student Experience
• AI Anxiety And Stress
• AI Literacy
• Student Engagement

Connected Articles

• Ortiz Bonnin Chat Or Cheat Chatgpt Dishonesty 2025 — academic dishonesty, risk perceptions, and ChatGPT usage
• AI Tools Academic Work Cheating 2026 — whether using AI tools counts as cheating
• Moral Panic GenAI Classroom — moral panic and appropriate GenAI use
• Best Response Student AI Dialog 2026 — student reasoning about AI use
• Academic Dishonesty Automated Proctoring AI 2026 — automated proctoring and dishonesty
• AI Anxiety Strategic Regulation Writing 2026 — AI anxiety as a productive signal

Citation

Qu, Y., & Wang, J. (2026). To disclose or not to disclose: Peer influence and psychological factors in students' use of generative artificial intelligence. British Journal of Educational Psychology. https://doi.org/10.1111/bjep.70086