Academic Fraud: Fraud Hexagon Perspective and Artificial Intelligence

Abstract

The rapid adoption of artificial intelligence (AI) in higher education has transformed learning and assessment practices, while simultaneously raising concerns about academic integrity. The increasing ease of accessing AI-based tools may alter students’ perceptions of academic misconduct and provide new rationalizations for unethical behavior. Against this backdrop, this study examines academic fraud among university students using the Fraud Hexagon Theory, with artificial intelligence incorporated as a moderating variable. Employing a quantitative research design, primary data were collected through a questionnaire administered to 189 accounting students in Surabaya who had taken ethics-related courses and were familiar with the use of AI tools. The data were analyzed using Structural Equation Modeling with the Partial Least Squares approach (SEM-PLS). The results indicate that pressure, rationalization, and collusion have a significant positive effect on academic fraud, whereas opportunity, capability, and arrogance do not show a significant influence. Furthermore, artificial intelligence is found to moderate only the relationship between rationalization and academic fraud, suggesting that AI strengthens students’ cognitive justification for unethical behavior rather than structural or situational antecedents. These findings imply that the integration of AI into academic activities requires adequate supervision and ethical guidance to prevent misuse. This study contributes to the academic integrity literature by extending the application of the Fraud Hexagon Theory to the academic context and by highlighting the contextual role of artificial intelligence in shaping academic fraud behavior in higher education.