AI-based digital cheating at university, and the case for new ethical pedagogies

Synthesis: Leaton Gray, Edsall and Parapadakis argue that generative AI has not created a crisis of Academic Integrity so much as exposed one: massified, standardised and depersonalised assessment regimes already measured little more than the ability to locate, compile and paraphrase information, which is exactly what large language models now do cheaply and convincingly. Working from Ajzen's Theory of Planned Behaviour, Bandura's self-efficacy theory and situational crime prevention, they ask how far assessment practice creates the conditions for AI-facilitated misconduct and how ethical pedagogy might mitigate it. Their answer is grounded in the published case of Baird and Clare (2017), an Australian business capstone where 25 situational prevention techniques reportedly cut misconduct cases from 183 to 27 within a year. Detection and online proctoring, they conclude, are unreliable and anti-educational, since governance built on suspicion deepens estrangement between students and institutions; the sustainable path is assessment redesigned around authenticity, personalisation and iterative engagement.

Core Argument

1. AI lowers the cost of misconduct that was already endemic. AI-generated text has passed as human-authored in up to 80% of cases (Cotton et al., 2023; Eke, 2023; Elkhatat et al., 2023; Liu et al., 2024), and LLMs simply join the 'e-cheating' methods catalogued by Dawson (2020) before generative AI arrived. Cheating studies reach back to Campbell (1935) and Bowers (1964), and Murdock and Anderman (2006) put the share of students who had cheated at secondary school and university as high as 80–90%. AI amplifies an existing condition, which is why banning tools cannot reach its source.
2. Cheating is planned behaviour, driven by motivation and perceived control. Combining Ajzen's (1991, 2005) Theory of Planned Behaviour with Bandura's (1977) theory, the authors trace an external locus of control linked to dishonesty (Leming, 1980) and an internal locus to honesty (Rinn et al., 2014): when students feel little Learner Agency, the perceived barriers to cheating fall. Murdock and Anderman's (2006) model frames the decision as three questions: "What is my purpose?", "Can I do this?", "What are the costs?". The counter-intuitive evidence is Krou et al.'s (2021) meta-analysis, where Self-Efficacy correlates negatively with cheating while actual ability does not inversely correlate with it at all: a capable, confident student who reads an assessment as unfair may cheat to regain control.
3. Detection and proctoring are a losing position. Transformer models paraphrase rather than copy, so their output matches nothing in a plagiarism database, and the evidence cited puts machine detection at about 80% against 78.4% for human reviewers (Wahle et al., 2022), too narrow a margin to carry a misconduct finding. Digital proctoring, delivered through tools such as Turnitin and Safe Exam Browser, relies on static surveillance and basic liveness checks that can be bypassed, assumes students are potential cheaters rather than active Learners (Lee and Fanguy, 2022), and is invasive enough to become a target for subversion (Simko et al., 2024).
4. An assessment that AI can answer convincingly is an intellectually trivial assessment. If a model can source information, assemble a coherent narrative and paraphrase it without understanding the subject, and thereby deceive an assessor, what is measured is keyword identification and compilation, not knowledge or ability. The failure belongs to the assessment, not the student, and predates AI: the technology has merely exposed the superficiality of standardised, anonymously marked tasks designed for administrative convenience rather than pedagogical fidelity (Blackie, 2024; Kramm and McKenna, 2023).
5. Institutional procedure makes enforcement unlikely even when cheating is suspected. Quality assurance protocols demand forensic evidence at a level that deters staff from pursuing investigations (Brigham and Ziebart, 2020; Keith-Spiegel et al., 1998), yet unaddressed cheating tends to proliferate (Packalen and Rowbotham, 2022). Competition among contract cheating providers, including platforms that use AI to automate client communication, has cut the cost of commissioned work inside the 'gig academy' (Gaumann and Veale, 2024; Kezar et al., 2019; Sweeney, 2023). The result is a war of attrition (Keir and Ives, 2022) that lecturers cannot win by spending more time on detection as cohorts grow.
6. Cheating is not generally seen as inherently immoral, and AI leaves its social costs untouched. The authors find it "surprisingly" clear that cheating does not evoke strong moral outrage among students (Ashworth et al., 1997; Marsden et al., 2005), a view shared by academics (Godecharle et al., 2018; Martinson et al., 2005) and society at large (Henle et al., 2019; Wood et al., 2007). AI reduces the time, effort and money cheating costs but barely touches its social costs, such as humiliation and ostracism, and that asymmetry is the opening for pedagogy: where students identify with an aspirational ideal future self (Boyatzis and Akrivou, 2006) as members of an intellectual community that rejects cheating, the behaviour can be discouraged before it is planned.

Theory, method and what the article is

This is a conceptual article rather than an empirical study. It poses two research questions, on how far current assessment practice breeds AI-facilitated misconduct and how validity-based ethical pedagogy might mitigate it, builds its argument from the Theory of Planned Behaviour, self-efficacy theory and situational crime prevention (Clarke, 2017), and grounds it in a case drawn from the published literature rather than data the authors collected. They also say which theories they set aside: General Strain Theory (Agnew and Brezina, 2019) and Neutralisation Theory (Sykes and Matza, 1957) address strain most students do not face, while Routine Activity Theory (Cohen and Felson, 1998) and Social Learning Theory capture less of the individual decision-making involved. A footnote concedes that external factors such as variable teaching quality and poor learning environments were left untheorised, and the authors add that no cross-culturally stable definition of cheating exists, since some systems emphasise memorisation and reproduction while others require critical interpretation and originality (Wanyama Wanyonyi, 2024).

The Baird and Clare case: prevention instead of punishment

The article's concrete evidence comes from Baird and Clare's (2017) crime prevention case study of an Australian university business capstone, in which 25 situational crime prevention techniques adapted from Clarke (2017) were applied to Assessment design so as to raise the effort and risk of cheating, reduce its rewards, reduce provocations and remove excuses. Reported misconduct cases fell from 183 to 27 within a year. The case does not concern AI or LLM use directly, but the authors treat it as a framework for the social and pedagogical conditions that do apply.

The mechanisms are what the article's proposals generalise from. A computer-based Simulation tracked student interactions; a red-flag system detected discrepancies and work that looked too expert; random reassignment of team members, a "board shake-up", raised both effort and risk. Students received easily digestible information on misconduct standards, which the case authors describe as priming a student's conscience, and course materials that students had published online, in some cases for payment, were met with takedown notices under copyright law. Weekly in-class invigilated tests raised the effort required to cheat while restoring personal oversight and lecturer-student interaction. The limits are recorded too: granular tracking suited a structured simulation but may not transfer to essays or project work, and bespoke single-use materials conflict with current workload models.

Rehumanising assessment: what the authors propose

The prescriptions follow from the motivational analysis rather than from detection. Universities should raise the perceived purpose of assessment, build self-efficacy, and increase the perceived social cost of cheating, and the authors warn that addressing only one or two of these will fail because the model needs all three. Dawson et al.'s (2024) validity framework is presented as the structured route to aligning assessment with intrinsic motivation, and the article offers five discipline-specific redesigns:

• STEM: timed problem-solving exams become open-book problem solving with a written reflection on the student's own process.
• Humanities: summative essays become collaborative research projects using primary and secondary sources, such as a group digital exhibit built on archival research.
• Business: case study analysis becomes real-world simulation with peer-reviewed presentations, such as a market strategy pitched to peers and instructors.
• Social Sciences: theoretical essays become community-based action research with local partner organisations.
• Creative Arts: portfolio submissions become iterative peer-reviewed design processes with Feedback from peers and industry professionals.

The common commitments are authentic real-world tasks, personalisation to the student's interests, and process-oriented work that carries feedback through the middle of a task rather than only at its end. Formative assessment is the corrective to the depersonalised lecture-and-exam pattern massification produced, and the authors draw on pandemic experience of remote teaching, where low-stakes iterative work and weekly collaborative assignments increased intrinsic motivation and ownership (Surahman and Wang, 2022). Viva-style examinations and peer-mediated assessment are argued to reduce reliance on AI tools, and digital divides in devices, connectivity and caring responsibilities must be addressed, since equitable access is a precondition of integrity.

Limits and what remains untested

The authors describe their contribution as a theoretical framing and call for empirical work testing validity-based interventions across disciplines and cultures. Three constraints travel with the argument. The 183-to-27 result comes from one Australian business capstone, is reported by that study's authors rather than replicated, and is not AI-specific; the article's own hedge is that its mechanisms may not transfer to less structured assessment. And the alternative to detection is expensive in precisely the resource universities are economising on: bespoke materials, single-use tasks, weekly invigilated tests and personalised feedback all consume staff time. That is why they frame the problem as a question about what higher education is for, and argue that AI Governance resting on suspicion damages Trust in ways the academy will not easily repair.

Connected Concepts

• Academic Integrity — the reliability and validity of assessment in its wider societal context
• Assessment Validity — the paper's central design criterion for resisting AI-facilitated misconduct
• Assessment — massified, standardised and depersonalised forms named as the underlying vulnerability
• Authentic Assessment — real-world, personalised tasks that raise the perceived purpose of assessment
• Formative Assessment — low-stakes iterative work offered as an antidote to credential-driven instrumentalism
• Reducing AI Misuse — prevention through design rather than prohibition or surveillance
• AI Detection — critiqued as unreliable and as an anti-educational basis for governance
• Remote Proctoring — invasive monitoring treated as a target for subversion rather than a deterrent
• Generative AI — positioned as an amplifier of existing structural weaknesses
• Motivation — extrinsic credentialism and mastery orientation as predictors of cheating
• Self-Efficacy — belief in one's own competence and its complex relation to dishonesty
• Self-Determination Theory — needs for control and self-determination behind the choice to cheat
• Critical Pedagogy — the ethical realignment of the university's purposes the article argues for
• Trust — erosion of the student–teacher relationship under suspicion-based governance
• Student Experience — depersonalised marking and surveillance as drivers of student alienation

Connected Articles

• A Comprehensive Review of the Changing Landscape of Academic Dishonesty in Automated Proctoring in the Era of Artificial Intelligence — Reviews the proctoring systems whose technical and ethical limits this article argues against
• Detecting the Undetectable? Reassessing Academic Misconduct Procedures in the Era of Generative AI — Shares the conclusion that procedures built on detectability cannot hold
• Beyond Detection: Redesigning Authentic Assessment in an AI-Mediated World — Redesigns authenticity as the alternative to policing authorship
• The Impact of Generative AI on Academic Integrity of Authentic Assessments Within a Higher Education Context — Experimental evidence that markers cannot reliably distinguish GenAI input in authentic assessments
• From authentic products to authenticated processes: a systematic conceptual review of authentic assessment in AI-rich — Product resemblance as an unreliable signal of capability, the same claim in assessment terms
• The psychological mechanisms and behavioral determinants of academic integrity in the age of artificial intelligence — Tests the motivation and self-efficacy pathway this article theorises
• Purpose Before Policy: Academic Integrity, Generative AI, and Rhetorical Stance — Purpose before policy, matching the claim that reform is a question about the university's mission
• Is using artificial intelligence tools for academic work cheating? Student perceptions, ethics, and the impact — Student perceptions of where AI assistance ends and misconduct begins

Citation

Leaton Gray, S., Edsall, D., & Parapadakis, D. (2025). AI-based digital cheating at university, and the case for new ethical pedagogies. Journal of Academic Ethics, 23, 2069–2086.