Generative AI-Assisted Academic Writing Experience and University Students' Academic Self-Efficacy: Parallel Indirect...

Generative artificial intelligence (GenAI) is increasingly used in academic writing, yet its associations with students' broader academic emotions and capability beliefs remain unclear. Drawing on control-value theory and social cognitive theory, this cross-sectional study examined positive and negative academic emotions as parallel statistical pathways between GenAI-assisted writing experience and general academic self-efficacy. Participants were 1128 students from universities in eastern China. A covariate-adjusted parallel pathway model was estimated with 5000 bootstrap resamples. GenAI-assisted writing experience was positively associated with self-efficacy (total association B = 0.733, β = 0.670) and broader positive emotions (B = 0.778, β = 0.631), and negatively associated with broader negative emotions (B = -0.457, β = -0.298). The positive-emotion indirect association was 0.471 (standardized β = 0.431), 95% CI [0.409, 0.531], whereas the negative-emotion indirect association was 0.033 (standardized β = 0.030), 95% CI [0.018, 0.052]. The direct association remained positive (B = 0.229, β = 0.209). After positive academic emotions were controlled, the coefficient linking GenAI-writing experience with negative emotions changed from negative to positive, indicating a statistical suppression pattern. A sensitivity model excluding emotional-motivation items reproduced both indirect associations. Given the limited discriminant validity between positive emotions and self-efficacy and the concurrent self-report design, the larger positive indirect association warrants cautious interpretation. The findings indicate that GenAI-assisted writing experience is associated with broader academic emotions and efficacy beliefs while retaining a smaller residual negative-emotion component.