The dark tetrad as associated factors in generative AI academic misconduct: insights beyond personal attribute variables - Sun, Rongjian; Tang, Minmin; Zhou, Junjun; Loan, Nguyen Thi Thuy; Wang, Cheng-Yen (Frontiers in Education, 2025)
The rise of generative artificial intelligence (AI) tools has reshaped the academic integrity landscape, introducing new challenges to maintaining honesty in scholarly work. Unlike traditional plagiarism, which typically involves copying existing text, generative artificial intelligence-generated content often appears sufficiently original to evade detection systems. This underscores the necessity of investigating the factors that contribute to such misconduct. This study explores the factors associated with Generative AI academic misconduct among university students in Taiwan, focusing on personality traits from the Dark Tetrad–Machiavellianism, narcissism, psychopathy, and sadism–alongside other personal attribute variables. Data were collected from 812 participants (Meanage = 24.86), comprising 439 females and 373 males, including 362 undergraduates and 450 graduate students. The results indicate that narcissism, psychopathy, and sadism significantly are significantly associated with Generative AI academic misconduct, while gender, educational level, grade point average, and Machiavellianism are not significant associated factors. These findings highlight the limited relevance of traditional personal attributes as associated factors in the context of generative AI and emphasize the need for targeted interventions to address personality-driven behaviors in mitigating the risks of academic misconduct.