From Environment to Engagement: Innovative Insights Into AI ‐Mediated Language Learning Through Psychological Mechanisms
TL;DR
Investigation of associations among teacher enthusiasm, learner growth mindset, and student engagement in AI‐mediated language learning finds that willingness to communicate, resilience and creative self‐efficacy are interconnected, with engagement occupying a central position within the network.
Abstract
AI is increasingly used in language learning, yet the psychological mechanisms underlying student engagement remain insufficiently understood. This study investigates the associations among teacher enthusiasm, learner growth mindset, and student engagement in AI‐mediated language learning, with a focus on the mediating roles of willingness to communicate, resilience and creative self‐efficacy. A total of 699 university students participated in a cross‐sectional survey, and the data were analysed using structural equation modelling and psychological network analysis to examine indirect pathways and networked associations among environmental, cognitive and psychological factors. The results indicate that higher levels of teacher enthusiasm and stronger growth mindset are positively associated with psychological variables, which in turn are linked to higher engagement. Network analysis further reveals that willingness to communicate, resilience and creative self‐efficacy are interconnected, with engagement occupying a central position within the network. These findings highlight the importance of considering multiple psychological mechanisms when exploring engagement in digitally enhanced learning contexts and extend social cognitive theory by illustrating the interactive contributions of environmental and individual cognitive factors. These findings extend social cognitive theory by showing how environmental and individual factors jointly shape engagement through multiple psychological mechanisms.