Exploring Chinese Secondary <scp>EFL</scp> Students' Self‐Regulated Learning and Task Engagement in <scp>AI</scp> ‐Assisted Classrooms: A Latent Growth Curve Modelling Study
Liu Shi, Shengji Li, Jingjing Xing
Abstract
ABSTRACT 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.