Personality Predictors of Attitudes and Misconduct Behaviors Related to Generative Artificial Intelligence: Evidence from the HEXACO and Dark Triad Models

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 (β = 0.25, p < 0.001) and Openness to Experience (β = 0.36, p < 0.001) were significant positive predictors of favorable GAI attitudes. Regarding misconduct behaviors, Honesty-Humility (β = -0.36, p < 0.001), Agreeableness (β = -0.15, p < 0.001), and Conscientiousness (β = -0.25, p < 0.001) were significant negative predictors of GAI-related misconduct, while Narcissism (β = 0.24, p < 0.001) and Psychopathy (β = 0.25, p < 0.001) were significant positive predictors. Notably, GAI attitudes did not provide additional predictive value for misconduct beyond personality traits (ΔR² = .004, p = 0.55). These results contribute to the understanding of personality’s role in GAI adoption and unethical academic behaviors, with implications for the responsible integration and regulation of GAI in academic practices.