The relative importance of self-determined motivation for engagement in generative AI-supported English language learning: Insights from Japanese university students

Abstract

Both motivation and engagement play pivotal roles in second language (L2) learning within the fields of applied linguistics and digital applied linguistics. While previous studies in applied linguistics have accumulated findings on the motivation-engagement nexus, there remains a paucity of empirical research in digital applied linguistics grounded in robust theoretical frameworks for understanding the comprehensive relationship of both constructs due to its relatively recent emergence. The current study addresses the gap by employing a self-determination theory to investigate the relationship between self-determined motivation and engagement in L2 learning with the use of generative artificial intelligence (GenAI). Data were collected using a questionnaire from a total of 98 university students learning English as a foreign language in Japan. Results from correlation analysis showed that autonomous forms of regulation were strongly associated with three dimensions of engagement (i.e., behavioral, cognitive, and emotional). Furthermore, a series of multiple regression analysis with supplemental use of dominance analysis and random forest plots revealed that autonomous motivation accounted for nearly 80% of the variance in behavioral, cognitive, and emotional engagement, respectively. The findings highlight the key role of autonomous motivation, especially identified regulation, in facilitating active participation in L2 learning with GenAI. This article concludes with theoretical and pedagogical implications, emphasizing the viability of SDT in conceptualizing the motivation-engagement nexus in GenAI-supported L2 learning and the critical roles of teachers in helping students recognize the value of L2 learning with GenAI.