Artificial intelligence anxiety and academic burnout among Chinese postgraduate students: perceived stress and rumination as serial mediators.

Introduction: This study examined whether perceived stress and rumination help explain why postgraduate students with higher artificial intelligence (AI) anxiety also report greater academic burnout. Methods: We analysed data from a cross-sectional online questionnaire survey conducted among postgraduate students in Guangxi, China. The final sample included 1,501 postgraduate students. Participants completed measures of AI anxiety, academic burnout, perceived stress, and rumination. The four measurement models were examined in Mplus using weighted least squares mean and variance adjusted estimation, and serial indirect associations were tested with PROCESS Model 6 using 5,000 bootstrap samples. Results: Higher AI anxiety was associated with higher academic burnout, higher perceived stress, and stronger rumination. Perceived stress and rumination each showed significant indirect associations between AI anxiety and burnout, and the serial path through perceived stress and then rumination was also significant. The pattern remained after adjustment for sex, age, academic stage, discipline, prior AI-tool use, and weekly AI-tool use duration. Discussion: These findings indicate partial serial mediation and should be interpreted as cross-sectional indirect associations rather than evidence of temporal causality.