Artificial Intelligence-Generated Content Characteristics, Ethical Risk Perception, and Usage Intentions Among Chinese University Students
Zhuoying Yan · SAGE Open · 2026
This study examines undergraduate students’ intentions to use Artificial Intelligence-Generated Content by integrating the Technology Acceptance Model, Theory of Planned Behaviour, and Cognition-Affection-Conation framework. Using a cross-sectional survey design, data from 479 undergraduate students were analysed using partial least squares structural equation modelling. The results show that facility conditions and output quality were negatively associated with ethical risk perception, whereas anthropomorphism and task-technology fit were positively associated with it. Ethical risk perception was negatively associated with Artificial Intelligence-Generated Content usage intention both directly and indirectly through attitudinal ambivalence and subjective norms. Artificial intelligence literacy moderated the negative association between attitudinal ambivalence and usage intention, but did not significantly moderate the relationship between subjective norms and usage intention. Multi-group analysis further revealed that the negative association between facility conditions and ethical risk perception was stronger among students with agricultural hukou, whereas the positive association between anthropomorphism and ethical risk perception was stronger among students with non-agricultural hukou. These findings demonstrate the complex interplay among technological characteristics, ethical risk perception, psychological processes, and social background in shaping Artificial Intelligence-Generated Content adoption. The study extends research on Artificial Intelligence-Generated Content adoption by incorporating ethical risk perception into an integrated theoretical framework and identifying artificial intelligence literacy and hukou status as important boundary conditions in higher education contexts.