Beyond Using AI : Self‐Regulated Pathways to Engagement in AI ‐Assisted Language Learning
TL;DR
It is suggested that engagement in AI‐assisted language learning is associated with learners' AI‐related readiness, self‐regulatory processes and critical evaluation, and theoretical and pedagogical insights into self‐regulated AI‐supported language learning are provided.
Abstract
Artificial intelligence (AI) has increasingly reshaped language learning, yet how learners become engaged in AI‐assisted language learning remains insufficiently understood. Drawing on self‐regulated learning theory, this study examined the relationships among AI literacy, human‐AI trust, metacognitive strategies, critical thinking and engagement. A questionnaire survey was conducted with 768 undergraduate students who had experience with AI‐assisted language learning. Data were analyzed using structural equation modelling and fuzzy‐set qualitative comparative analysis. The structural equation modelling results showed that AI literacy and human‐AI trust were positively associated with both metacognitive strategies and critical thinking, which were further related to learner engagement. The mediation results indicated that metacognitive strategies and critical thinking served as significant linking mechanisms between AI‐related antecedents and engagement. The configurational analysis revealed that no single condition was necessary for high engagement, while five sufficient configurations were identified, highlighting multiple pathways to engagement. These findings suggest that engagement in AI‐assisted language learning is associated with learners' AI‐related readiness, self‐regulatory processes and critical evaluation. The study provides theoretical and pedagogical insights into self‐regulated AI‐supported language learning.