The relationship between AI anxiety and academic motivation among university students: the mediating role of emotion regulation and the moderating role of gender

Background Generative artificial intelligence (AI) is increasingly embedded in university students’ writing, information retrieval, knowledge organisation and task completion. Although AI tools may improve learning convenience and access to resources, they may also generate technological uncertainty, pressure to adapt to new competencies and concerns about future development. As an important negative emotional response in intelligent technology contexts, AI anxiety may be closely associated with university students’ academic motivation and psychological adaptation. Methods This cross-sectional questionnaire study recruited 1,484 university students in China using convenience sampling. Participants completed measures of AI anxiety, emotion regulation and academic motivation through the Wenjuanxing online survey platform. Descriptive statistics, Pearson correlation analysis and regression analyses were used to examine associations among variables. The PROCESS macro was used to test the mediation effect and the moderated mediation effect. All indirect effects were estimated using 5,000 bootstrap samples and 95% confidence intervals. Results AI anxiety was negatively associated with both emotion regulation and academic motivation, whereas emotion regulation was positively associated with academic motivation. Bootstrap analyses showed a significant negative indirect association between AI anxiety and academic motivation through emotion regulation. Gender significantly moderated the association between emotion regulation and academic motivation, with a stronger positive association among male students. Conclusion AI anxiety may constitute a psychological barrier to students’ motivational adaptation to AI-supported learning. Emotion regulation represents one psychological pathway linking AI anxiety to academic motivation, and gender constitutes a boundary condition for this association. These findings suggest that AI literacy education should integrate emotion-regulation and metacognitive support with differentiated learning assistance. Given the cross-sectional and self-reported nature of the data, the findings require further validation using longitudinal or experimental designs.