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Research Article

Cheating or not cheating? Rethinking AI-giarism and academic integrity through secondary students' ethical reasoning

Synthesis: Chan (2026) qualitatively analysed open-ended responses from 133 Hong Kong secondary students (aged 14–16) to show that reasoning about the ethicality of generative AI in homework resists simple "cheating or not cheating" binaries. Applying Waltzer and Dahl's moral-developmental framework, the study found students engage in conditional, context-sensitive ethical reasoning about purpose of use, learning impact, intent, and fairness, positioning themselves as active moral agents rather than passive rule-followers. The findings challenge fixed notions of AI-giarism and argue that Academic Integrity, Assessment design, and AI Literacy education must foreground ethical reasoning, transparency, and student agency.

Key Findings

  1. Conditional use dominates. Most secondary students evaluated AI use in a maths homework scenario as acceptable only under conditions—acceptable for inspiration, clarification, reference, or checking, but not for producing final answers or direct copying.
  2. Distinct ethical sub-criteria. Students weighed three factors in deciding whether AI use was cheating: purpose of use (62.4%), impact on learning (54.1%), and student intent (51.9%).
  3. Ethical reasoning beyond rules. Students judged acceptability through academic norms/conventions (48.1%), comparison with other tools like Google (31.6%), and accountability/consequences including fairness (23.3%).
  4. Intent as the moral boundary. Students consistently framed intent—learning-oriented Help Seeking versus effort-avoiding substitution—as the decisive factor distinguishing legitimate support from dishonesty.
  5. AI as a moral agent, not a fixed category. Roughly 9% of the sample rejected all AI use as undermining independent thinking; the majority treated academic misconduct as a negotiated moral space shaped by intent and condition rather than a fixed category.
  6. Fairness and equity concerns. Students worried AI could "trick the teacher" into overestimating understanding, disadvantage capable peers, or—alternatively—democratise access to help often unavailable without private tutoring.
  7. Emerging norms of transparency. Several students proposed disclosing AI use through footnotes or references, suggesting their ethical reasoning may run ahead of existing institutional policy.

Implications

  • School-level AI policy should reflect students' conditional reasoning rather than impose rigid bans or binary cheating definitions, explicitly addressing intent, purpose, and learning impact.
  • AI literacy education should prioritise ethical reasoning and self-regulation over mere compliance—helping students articulate why certain uses are problematic.
  • Detection- and surveillance-based responses to AI-giarism are insufficient because ethical status depends on intent and use, not observable output.
  • Involving students in integrity policy development can strengthen shared commitment and Trust, treating academic integrity as an investment in moral development rather than an external constraint.
  • Assessment design should move toward process-visible, reflective, and AI-resilient formats (e.g., justification of choices, evaluative judgement) that reward genuine learning.

Connected Concepts

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Citation

Chan, C. K. Y. (2026). Cheating or not cheating? Rethinking AI-giarism and academic integrity through secondary students' ethical reasoning. Computers & Education, 253, 105698.

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