Exploring Chinese Secondary EFL Students' Self-Regulated Learning and Task Engagement in AI-Assisted Classrooms: A Latent Growth Curve Modelling Study

The growing integration of artificial intelligence (AI) tools into English as a foreign language (EFL) instruction presents new opportunities for fostering students' self-regulated learning (SRL) and task engagement (TE). While prior research has shown that AI-assisted environments can enhance metacognitive monitoring and learning motivation, longitudinal evidence on how SRL and TE develop in tandem remains limited. To address this void, this study employed a parallel-process latent growth curve modelling (LGCM) approach to investigate the co-developmental trajectories of SRL and TE among 334 Chinese secondary school students enrolled in a semester-long AI-assisted EFL programme. Results indicated modest but significant growth in both SRL and TE, with substantial inter-individual variability. Positive correlations were found between the intercepts and slopes of the two constructs, supporting a dynamic reciprocal relationship. However, cross-domain negative effects suggested potential ceiling constraints among highly self-regulated or highly engaged learners. These findings underscore the importance of designing adaptive AI tools that account for diverse learner profiles and sustain long-term engagement and regulation.