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Research Article

Academic Integrity in the Age of AI: University Students' Study Practices and Ethical Judgments

Synthesis: López-López, Bru-Cordero, and Correa-Álvarez (2026) analysed survey data from 357 undergraduates across 14 programs at a Colombian public university to examine how students interpret academic integrity in relation to AI. They found AI use is routine and usually perceived as helpful for understanding content, yet students' ethical judgments remain divided—a slight majority rejected the idea that AI use is fraud, over one-third were undecided, and a smaller group endorsed it. The authors introduce the concept of pragmatic ambiguity to describe how students negotiate AI use between academic usefulness, uncertain institutional boundaries, and concerns about authorship and intellectual contribution.

Key Findings

  1. AI use is routine. Students across programs use tools like ChatGPT, Gemini, Copilot, and DeepSeek for brainstorming, explanation, translation, summarisation, coding support, feedback, and early drafting.
  2. Judgments are divided, not settled. A slight majority rejected the view that using AI in academic tasks is fraud; more than one-third were undecided; a smaller group endorsed the fraud framing.
  3. Use and perceived value drive permissiveness. More frequent AI use (χ² = 78.08, p < 0.001) and stronger perceived learning support were associated with more permissive integrity judgments; in ordered logistic regression, frequent use (OR = 0.669) and perceived learning support (OR = 0.581) lowered odds of stricter fraud judgments.
  4. Perceived Creativity reduction drives strictness. Students who believed AI reduced their creativity had significantly higher odds of endorsing stricter fraud judgments (OR = 1.498, p < 0.001).
  5. Undecided students are engaged, not disengaged. More than a third of the sample (n = 135) were undecided, yet a majority used AI almost always and nearly all reported it helped them understand topics—indecision reflects genuine ethical uncertainty, not inexperience.
  6. Independent study time correlated with stricter judgments (OR = 1.174, p = 0.033), suggesting students who invest more in independent preparation value process, effort, and accountability.
  7. Perceived dependence, not frequency, predicted negative consequences. Reported academic failure due to AI was strongly associated with perceived AI dependence (χ² = 25.44, Cramer's V = 0.267) but not with frequency of use.

Implications

  • The findings support a shift from a detection-centred to an authorship-centred integrity culture, orienting around disclosure, responsibility, and protecting meaningful student authorship.
  • Pragmatic ambiguity highlights the need for clearer institutional boundaries and assignment-level guidance on permissible AI use, rather than rigid prohibition.
  • Because perceived value and dependence shape judgment and consequences, AI literacy education and assessment design should foster self-regulated, non-substitutive use.
  • Findings from an underrepresented Latin American public-university context broaden global understanding of how norms form unevenly in higher education.
  • Institutions should attend to creativity concerns, which appear to sharpen students' own ethical evaluations, and should address inequitable access to academic support.

Connected Concepts

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Citation

López-López, E. M., Bru-Cordero, O. E., & Correa-Álvarez, C. D. (2026). Academic integrity in the age of AI: University students' study practices and ethical judgments. Trends in Higher Education, 5(2), 49.

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