The Role of Biological Rhythms in Generative AI Addiction: Associations with Chronotype and Sleep Quality

The rapid diffusion of Generative Artificial Intelligence (GenAI) technologies among university students has introduced a novel domain of technology-related behavioral risk. Although prior research on digital addiction has predominantly focused on psychological determinants of internet and smartphone overuse, the potential role of circadian preference and sleep in problematic GenAI use remains underexplored. This study examined associations between chronotype, sleep quality, and GenAI addiction. Using a cross-sectional survey, data were collected from 868 undergraduate students at a public university using the Artificial Intelligence Addiction Scale, the Composite Scale of Morningness, and the Pittsburgh Sleep Quality Index. According to results, evening-type students reported higher GenAI addiction scores than morning and intermediate types and poor sleep quality was positively associated with GenAI addiction. In regression models controlling for age, gender, and sleep quality, chronotype remained associated with GenAI addiction scores, whereas gender was not statistically significant. These findings indicate that circadian preference and sleep quality are relevant correlates of GenAI addiction and highlight the need for further longitudinal and multi-method research in higher education contexts.