Predictors of Self‐Regulated Learning in AI‐Supported Language Education: A Social Cognitive Theory Perspective

Guided by Social Cognitive Theory, this study explored how language learning curiosity, perceived teacher support, and AI literacy predict learners' self‐regulated learning (SRL) in AI‐supported language education, with grit and flow as mediators. Data collected from 1022 Chinese undergraduate students were analysed using descriptive statistics, confirmatory factor analysis, structural equation modelling with bootstrapped mediation analysis, and psychological network analysis via SPSS, AMOS, and R. The results showed that all three antecedent variables were positively associated with grit and flow, which in turn significantly mediated their influences on self‐regulated learning. Network analysis further revealed that self‐regulated learning was the most pivotal node among the six constructs. These findings suggest that effective AI‐supported language learning requires not only technological access, but also motivational, contextual, technological, and psychological resources to foster learners into agentic and self‐regulated language learners.