The dark tetrad as associated factors in generative AI academic misconduct: insights beyond personal attribute variables
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 co...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Education |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feduc.2025.1551721/full |
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| author | Rongjian Sun Minmin Tang Minmin Tang Junjun Zhou Nguyen Thi Thuy Loan Cheng-Yen Wang |
| author_facet | Rongjian Sun Minmin Tang Minmin Tang Junjun Zhou Nguyen Thi Thuy Loan Cheng-Yen Wang |
| author_sort | Rongjian Sun |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-08eea359fa9c4582b1506c2c7b7ad0e1 |
| institution | Kabale University |
| issn | 2504-284X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Education |
| spelling | doaj-art-08eea359fa9c4582b1506c2c7b7ad0e12025-08-20T03:33:18ZengFrontiers Media S.A.Frontiers in Education2504-284X2025-07-011010.3389/feduc.2025.15517211551721The dark tetrad as associated factors in generative AI academic misconduct: insights beyond personal attribute variablesRongjian Sun0Minmin Tang1Minmin Tang2Junjun Zhou3Nguyen Thi Thuy Loan4Cheng-Yen Wang5Department of Educational Psychology, Texas A&M University, College Station, TX, United StatesSchool of Psychology, Sichuan Normal University, Chengdu, ChinaSichuan Key Laboratory of Psychology and Behavior of Discipline Inspection and Supervision, Chengdu, ChinaSichuan Key Laboratory of Psychology and Behavior of Discipline Inspection and Supervision, Chengdu, ChinaDepartment of Marketing and Logistics Management, Chaoyang University of Technology, Taichung, TaiwanInstitute of Education, National Yang Ming Chiao Tung University, Hsinchu, TaiwanThe 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.https://www.frontiersin.org/articles/10.3389/feduc.2025.1551721/fullgenerative artificial intelligenceDark Tetradacademic performancegendereducational level |
| spellingShingle | Rongjian Sun Minmin Tang Minmin Tang Junjun Zhou Nguyen Thi Thuy Loan Cheng-Yen Wang The dark tetrad as associated factors in generative AI academic misconduct: insights beyond personal attribute variables Frontiers in Education generative artificial intelligence Dark Tetrad academic performance gender educational level |
| title | The dark tetrad as associated factors in generative AI academic misconduct: insights beyond personal attribute variables |
| title_full | The dark tetrad as associated factors in generative AI academic misconduct: insights beyond personal attribute variables |
| title_fullStr | The dark tetrad as associated factors in generative AI academic misconduct: insights beyond personal attribute variables |
| title_full_unstemmed | The dark tetrad as associated factors in generative AI academic misconduct: insights beyond personal attribute variables |
| title_short | The dark tetrad as associated factors in generative AI academic misconduct: insights beyond personal attribute variables |
| title_sort | dark tetrad as associated factors in generative ai academic misconduct insights beyond personal attribute variables |
| topic | generative artificial intelligence Dark Tetrad academic performance gender educational level |
| url | https://www.frontiersin.org/articles/10.3389/feduc.2025.1551721/full |
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