ArticleScientific reports2025
Personality traits measured by the HEXACO personality inventory and the dark triad predict university students' attitudes and misconduct behaviors related to generative artificial intelligence : Haiying Liang
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Moral Disengagement and Unethical Generative AI Use as the Chain Mediators Between Antagonistic Personality and Problematic Generative AI Use.Behavioral sciences (Basel, Switzerland) · 2026Article
- A configurational exploration of how personality traits influence GAI academic misconduct behaviors using fuzzy-set qualitative comparative analysis.BMC psychology · 2026Article
- In the future workplace: how the Dark Triad influences unethical behavior through the Fraud Pentagon among accounting students.Frontiers in psychology · 2026Article
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3 authors.
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Abstract
This study investigates how personality traits, specifically those measured by the HEXACO Personality Inventory and the Dark Triad, predict university students' attitudes toward generative artificial intelligence (GAI) and their engagement in GAI-related academic misconduct. The first objective was to develop and validate a Chinese-language scale to measure students' attitudes toward GAI in academic contexts. The newly developed GAI Attitudes Scale was tested for psychometric properties, showing high internal consistency (Cronbach's α = 0.92) and reliability. In the second part of the study, hierarchical linear regression analyses explored the relationship between personality traits and both GAI attitudes and misconduct behaviors. Findings indicated that Extraversion and Openness to Experience were significant positive predictors of favorable GAI attitudes. Regarding misconduct behaviors, Honesty-Humility, Agreeableness, and Conscientiousness were significant negative predictors, while Narcissism and Psychopathy were significant positive predictors. Notably, GAI attitudes did not provide additional predictive value for misconduct beyond personality traits. Taken together, the findings demonstrate that personality traits are central to understanding both the adoption and misuse of GAI in academic contexts, providing important insights for fostering ethical engagement with emerging technologies.
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