# A configurational exploration of how personality traits influence GAI academic misconduct behaviors using fuzzy-set qualitative comparative analysis

## TL;DR

This study explores how combinations of personality traits influence university students' misuse of generative AI in academic settings.

## Contribution

The study introduces a configurational approach combining HEXACO and Dark Triad traits to explain GAI academic misconduct.

## Key findings

- High GAI misconduct is linked to low Honesty–Humility and Conscientiousness combined with high Machiavellianism or Psychopathy.
- Low misconduct is associated with high Honesty–Humility, Conscientiousness, and Agreeableness alongside low Dark Triad traits.
- Personality traits interact synergistically to influence ethical engagement with AI technologies.

## Abstract

The rapid adoption of Generative Artificial Intelligence (GAI) in higher education has introduced new ethical challenges, particularly concerning students’ academic misconduct. While prior research has linked personality traits to unethical behavior, little is known about how different combinations of personality traits shape students’ misuse of GAI.

This study integrates the HEXACO model and the Dark Triad framework to examine the configurational effects of personality on GAI-related academic misconduct. A total of 864 university students completed questionnaires. Using fuzzy-set Qualitative Comparative Analysis, we identified multiple configurations leading to both high and low levels of GAI misconduct.

No single trait is sufficient to explain GAI-related academic misconduct. Rather, high misconduct consistently emerged from configurations characterized by low Honesty–Humility and Conscientiousness combined with high Machiavellianism or Psychopathy. In contrast, low misconduct was associated with configurations combining high Honesty–Humility, Conscientiousness, and Agreeableness with low levels of Dark Triad traits.

This study demonstrates that personality traits interact synergistically rather than independently to shape individuals’ ethical or unethical engagement with AI technologies. Moral restraint is sustained by both the presence of virtues and the absence of exploitative tendencies. The findings support the idea that moral integrity and self-regulation constitute foundational safeguards against unethical use of technology. These findings align with self-regulatory theories of academic dishonesty, reinforcing that individuals high in honesty and conscientiousness are less likely to rationalize or justify academic misconduct even when new technological affordances make it easier. The study therefore advances theoretical understanding by integrating personality frameworks within a configurational paradigm and offers practical insights for developing personality-informed ethics education.

The online version contains supplementary material available at 10.1186/s40359-026-04186-1.