Problematic generative AI use and mental health risks among Chinese university students: latent profiles, network structure, and health literacy as a modifiable resource

Abstract

Objective Generative artificial intelligence (GenAI) has become a routine academic tool for university students, but dysregulated use may co-occur with anxiety, sleep problems, psychological distress, and impaired academic functioning. This study examined GenAI-related digital behavior as a public health issue and assessed whether health literacy and self-regulated learning were associated with lower-risk profiles. Methods A cross-sectional survey was conducted among Chinese university students from six universities in eastern, central, and western China. A total of 2,140 questionnaires were submitted; after four-step data-quality screening, 1,872 valid responses were retained. Measures assessed GenAI-use dysregulation, academic anxiety, sleep problems, psychological distress, health literacy, self-regulated learning, academic engagement, and demographic and use-related covariates. Latent profile analysis identified GenAI-use and mental-health risk profiles. Profile differences were examined using omnibus and post hoc tests, multinomial logistic regression examined external factors associated with profile membership, and regularized network analysis identified central and bridge nodes. Results A four-profile solution showed the best balance of fit, entropy, class size, and interpretability (entropy = 0.891). The profiles were low-risk adaptive users (39.9%), anxiety-prone dependent users (25.7%), sleep-disrupted overusers (19.8%), and high-risk dysregulated users (14.6%). Confirmatory factor analysis supported the measurement structure of the adapted and selected scales (CFI = 0.958, TLI = 0.951, RMSEA = 0.036, SRMR = 0.041), and Harman's single-factor test did not indicate serious common method bias. Higher health literacy and self-regulated learning were associated with greater odds of low-risk rather than high-risk membership. Network analysis identified loss of control, academic worry, delayed bedtime, and academic avoidance as central or bridge nodes, whereas credibility evaluation and time management showed negative bridge expected influence. Conclusion GenAI-related digital behavior among university students varied in ways that involved dysregulated use, academic anxiety, sleep problems, distress, and academic functioning. These findings may help universities identify students who report difficulty controlling AI use, academic worry, delayed bedtime, academic avoidance, or trouble setting limits on AI use. They also point to health literacy, self-regulation, and time management as candidate resources for future longitudinal and intervention studies.