Dark Tetrad traits and problematic generative artificial intelligence use: the serial mediating role of individualism and intolerance of uncertainty

With the rapid integration of generative artificial intelligence into daily life, understanding the personality-based factors underlying problematic usage patterns has become increasingly important.

The present study investigated the relationships between dark personality traits (narcissism, Machiavellianism, psychopathy, and sadism) and problematic generative artificial intelligence use (PGAIU), with a particular focus on the serial mediating role of individualism and intolerance of uncertainty.

Data were collected from a large adult sample of generative artificial intelligence (GAI) users (N = 677; 50% men; M_age = 39.87) using self-report instruments.

Path analysis revealed that narcissism was positively associated with PGAIU through both direct and indirect pathways, whereas sadism demonstrated a small but significant total effect without identifiable direct or indirect mechanisms. Vertical individualism and intolerance of uncertainty were consistently and positively related to PGAIU across the total sample and sex-based groups. Further analyses suggested that, among women, the association between narcissism and PGAIU was fully indirect, operating through vertical individualism. In addition, psychopathy showed a divergent pattern among men, with a direct negative association with PGAIU alongside an indirect positive association via intolerance of uncertainty-related pathways. These patterns should be interpreted with caution, as full measurement invariance across sex was not supported.

Overall, the findings highlight the central role of individualistic value orientations and uncertainty-related processes in explaining how dark personality traits contribute to PGAIU. These results offer initial insights, within this sample and with these measures, into potential psychological mechanisms underlying dysregulated engagement with emerging GAI technologies. Replication in independent samples is needed before stronger conclusions can be drawn.