Unmasking academic cheating behavior in the artificial intelligence era: Evidence from Vietnamese undergraduates

作者：Nguyen Manh Hung, Daisaku Goto · 发表于：Education and Information Technologies · 年份：2024 · DOI：10.1007/s10639-024-12495-4 · 被引用次数：63 · 研究领域：Academic integrity and plagiarism、Artificial Intelligence in Healthcare and Education、Imbalanced Data Classification Techniques

Abstract The proliferation of artificial intelligence (AI) technology has brought both innovative opportunities and unprecedented challenges to the education sector. Although AI makes education more accessible and efficient, the intentional misuse of AI chatbots in facilitating academic cheating has become a growing concern. By using the indirect questioning technique via a list experiment to minimize social desirability bias, this research contributes to the ongoing dialog on academic integrity in the era of AI. Our findings reveal that students conceal AI-powered academic cheating behaviors when directly questioned, as the prevalence of cheaters observed via list experiments is almost threefold the prevalence of cheaters observed via the basic direct questioning approach. Interestingly, our subsample analysis shows that AI-powered academic cheating behaviors differ significantly across genders and grades, as higher-grade female students are more likely to cheat than newly enrolled female students. Conversely, male students consistently engage in academic cheating throughout all grades. Furthermore, we discuss potential reasons for the heterogeneous effects in academic cheating behavior among students such as gender disparity, academic-related pressure, and peer effects. Implications are also suggested for educational institutions to promote innovative approaches that harness the benefits of AI technologies while safeguarding academic integrity.