Empowering the autonomous learner: How AI-assisted language learning environments shape self-regulation,autonomy,and self-directed behaviors

Abstract

The rapid integration of artificial intelligence (AI) into language education has transformed how learners manage and direct their learning processes. Despite the growing adoption of AI-assisted tools, empirical understanding of their psychological and behavioral impacts remains incomplete. This study investigated how engagement in AI-assisted language learning environments shapes learners’ self-regulation, autonomy, and self-directed learning behaviors. Drawing on self-determination theory and Zimmerman’s model of self-regulated learning, the research employed a quantitative design with data collected from 736 Chinese university students using validated questionnaires measuring AI engagement, self-regulation, self-directed learning, and learner autonomy. Structural equation modeling (SEM) and correlational analyses were conducted using SPSS (v27) and AMOS (v24). Results indicated strong positive correlations between AI engagement and self-regulation, self-directed learning, and autonomy. Moreover, AI engagement significantly predicted learners’ self-regulation and autonomy, whereas self-regulation partially mediated the relationship between AI engagement and self-directed learning. These findings suggest that AI technologies, when employed as autonomy-supportive tools, can strengthen learners’ metacognitive awareness, intrinsic motivation, and independence in language learning. The study offers theoretical insights into digital self-regulated learning models and provides practical implications for educators seeking to integrate AI systems in ways that foster sustainable learner autonomy.