["European Journal of Education, Volume 61, Issue 3, September 2026. ", "\nABSTRACT\nThe growing use of artificial intelligence (AI) in English as a Foreign Language (EFL) education contexts has reshaped how learners interact, stay motivated and manage their learning experiences. However, limited empirical work has examined how social, motivational, and methodological factors operate together to explain learner engagement and emotional regulation in AI‐enhanced EFL education contexts. This study explored the relationships among social interaction quality, learner self‐efficacy, task motivation, perceived methodological fairness of AI‐supported assessment, behavioural engagement and psychological regulation. Survey data were collected from 497 Chinese undergraduate EFL learners who had prior experience with AI‐assisted English learning. Structural equation modelling (SEM) was conducted using AMOS version 24 to test both direct and indirect relationships among the study variables. The results showed that social interaction quality was positively related to self‐efficacy, task motivation and perceived methodological fairness. Self‐efficacy and task motivation emerged as strong predictors of behavioural engagement, while perceived methodological fairness played a central role in predicting psychological regulation. The findings suggest that learner engagement in AI‐enhanced EFL settings develops through motivational and emotional pathways rather than through technology use alone. The study offers theoretical insights into engagement and social cognitive perspectives and provides practical guidance for the design and implementation of AI‐supported instruction and assessment in EFL classrooms.\n"]