Beyond Frequency: Generative AI Engagement Quality, Psychological Well-Being, and Digital Inequality Among Chinese University Students

Generative artificial intelligence (GenAI) is now embedded in university students’ academic and everyday lives, yet frequency-based measures provide limited insight into why similar levels of use are associated with different psychological well-being outcomes. Using cross-sectional survey data from 3283 university students enrolled predominantly at institutions in Guangxi, China, this study examined GenAI engagement at three levels: overall use frequency, specific use purposes, and engagement quality reflected in self-perceived AI competence, self-reported critical AI literacy, and AI unavailability anxiety. Covariate-adjusted regressions, parallel statistical indirect-path models with 5000 bootstrap resamples, moderation analyses, and family-resource models were estimated. Overall use frequency was not associated with depressive symptoms or life satisfaction in baseline models. By contrast, use purposes showed heterogeneous associations, and engagement-quality indicators were more consistently related to well-being. More frequent use was associated with greater perceived competence and literacy but also with greater unavailability anxiety. Favorable indirect associations through competence and literacy partly offset the adverse association through anxiety, although the total indirect association with depressive symptoms was not statistically significant. The inverse association between self-perceived AI competence and depressive symptoms was statistically significant at low, but not average or high, levels of psychological capital, while family economic, cultural, and network resources showed distinct associations with competence and literacy. These findings direct attention beyond GenAI exposure frequency to the quality of engagement and the institutional conditions supporting equitable access to critical AI literacy.