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Latent profiles of generative artificial intelligence use among Chinese college students: Associations with
Haoyang Shi1, Tongyi Zhang1, Zihao Wang1
1School of Psychology, Northwest Normal University, Lanzhou, China.
Background:
While generative artificial intelligence (GenAI) has been rapidly adopted by college students, its relationship with mental health remains unclear. Most studies treat AI users as homogeneous, overlooking heterogeneity in GenAI use. This study identified latent profiles of GenAI use among Chinese college students and examined their associations with depression and anxiety.
Methods:
A cross-sectional survey of 5748 Chinese college students assessed AI usage, motivations, AI literacy, dependency, and symptoms of depression and anxiety using the Beck Depression Inventory-II and Beck Anxiety Inventory. Latent profile analysis identified usage patterns. An exploratory random-forest classifier with SHapley Additive exPlanations (SHAP) evaluated whether these profiles could be predicted from psychological and demographic correlates not used for profile construction and identified key distinguishing factors.
Results:
Four profiles were identified: Rational-Tool, Moderate-Recreational, Problem-Dependent, and Light-Exploratory. The Problem-Dependent profile (15%), characterized by high escapism motivation, low AI literacy, and high dependency, showed the highest depression and anxiety levels, whereas the Rational-Tool profile, characterized by high AI literacy and instrumental motivation, showed the most favorable outcomes. The profiles were recovered with good discrimination (accuracy = 0.81; macro-average AUC = 0.89), with the Problem-Dependent profile being the most distinguishable (AUC = 0.94). SHAP analysis identified depression, smartphone addiction, and executive-function difficulties as key correlates of the Problem-Dependent profile, whereas conscientiousness was central to the Rational-Tool profile.
Conclusions:
GenAI use profiles were associated with different levels of depression and anxiety among college students. Person-centered prevention strategies should focus on usage motivation, AI literacy, and dependency rather than frequency alone.