When the Machine Writes the Answer: A PRISMA-Guided Systematic Review of the Fraud Hexagon and Academic Fraud in the Era of ChatGPT
DOI:
https://doi.org/10.61656/sbamr.v7i1.532Keywords:
Academic Fraud, Accounting Students, ChatGPT, Fraud Hexagon, Generative Artificial Intelligence, PRISMAAbstract
Purpose: This study systematically reviews recent empirical evidence on the six fraud hexagon factors (pressure, opportunity, rationalisation, capability, arrogance, and collusion) as drivers of academic fraud among university students, with particular attention to online learning and the misuse of ChatGPT and other generative artificial intelligence tools. It responds to inconsistent findings in accounting education research and to growing concern about the integrity of future accounting professionals.
Method: The review follows the PRISMA 2020 statement. Five search strings were run in the Crossref database for peer-reviewed journal articles published between 2020 and 2024 with abstracts available, and citation tracking added key studies cited in the original research proposal. Records were de-duplicated, screened by title, and assessed for eligibility against predefined criteria. The included studies were coded by country, sample, design, learning context, and the direction of each fraud factor, and were synthesised through vote counting and thematic analysis.
Findings: The classic fraud triangle factors, namely pressure, opportunity, and rationalisation, remain the most frequently supported drivers, especially in online examinations and in the misuse of ChatGPT, where grade pressure, workload, easy access, and the belief that artificial intelligence is merely a tool are recurring themes. Capability and collusion are the most consistent of the newer hexagon factors when all six are modelled together, whereas arrogance is rarely significant. Findings within full hexagon models are fragmented, which suggests overlap among the factors and sensitivity to context and measurement.
Implication: Universities and accounting programmes should redesign assessment to reduce opportunity, publish clear policies on acceptable artificial intelligence use, address the rationalisations that normalise misuse, and design group work that discourages collusion. Ethics education should explicitly connect academic integrity with the professional code of ethics for accountants. Researchers should measure behaviour as well as intention and test interactions among the fraud factors.
Originality: The review is among the first to synthesise fraud hexagon evidence on academic fraud across conventional, online, and generative artificial intelligence settings, to show how the relevance of each factor shifts across these contexts, and to propose an agenda for studying artificial intelligence-assisted academic fraud among accounting students in Indonesia.
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