AI literacy and psychosocial factors shaping Chinese university students’ attitudes and behavioral intentions toward generative AI use

Abstract

Generative artificial intelligence (GenAI) is reshaping higher education by influencing students’ learning, cognition, and academic decision-making. Understanding the factors that associated with students’ acceptance of this technology is crucial for its successful integration. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating attitude as a mediator and AI literacy as an antecedent to investigate psychological factors associated with GenAI adoption among Chinese university students. A cross-sectional survey design was employed. Data were collected from 1,536 Chinese university students via an online questionnaire. The instrument included validated scales measuring AI literacy (awareness, evaluation, ethics), UTAUT constructs (performance expectancy, effort expectancy, social influence, facilitating conditions), attitude, behavioral intention, and use behavior. Partial least squares structural equation modeling (PLS-SEM) was used to test the hypothesized relationships. Performance expectancy (β = 0.345, p < 0.001), social influence (β = 0.154, p < 0.001), and facilitating conditions (β = 0.118, p < 0.05) were positively correlated with students’ attitudes toward GenAI. Performance expectancy (β = 0.266, p < 0.001), effort expectancy (β = 0.078, p < 0.05), and social influence (β = 0.283, p < 0.001) were linked to behavioral intention. Attitude was positively associated with behavioral intention (β = 0.192, p < 0.001), whereas its direct association with use behavior was small and non-significant after FDR correction (β = 0.060, p > 0.05); instead, behavioral intention showed a strong positive association with use behavior (β = 0.257, p < 0.001). Among AI literacy dimensions, awareness (β = 0.137, p < 0.001) and evaluation (β = 0.101, p < 0.001) were positively associated with attitudes, while ethics demonstrated a non-significant negative relationship (β = − 0.038, p = 0.148). The model explained 50.7% of the variance in attitude and 47.6% in behavioral intention. The findings presented herein highlight the relevance of AI awareness, evaluative competence, performance expectancy, and social endorsement for understanding students’ positive evaluations and intentions regarding GenAI use. Attitude emerges as a central affective correlate that connects these cognitive appraisals with students’ reported behavioral intentions. Taken together, these patterns may inform efforts to design AI literacy initiatives in university curricula and supportive learning environments that emphasize informed awareness, critical evaluation, and ethical reflection, thereby fostering more psychologically informed and responsible engagement with GenAI in higher education.

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