When GenAI chatbots meet self-determination: unpacking Chinese EFL learners’ engagement in chatbot-assisted language learning
WU Xue-qing, Rui Li, Choon Lang Quek
Abstract
While generative artificial intelligence (GenAI) chatbots show transformative potentials in English as a foreign language (EFL) education, critical gaps persist in understanding how chatbot-assisted language learning affect multidimensional learner engagement. Grounded in self-determination theory (SDT), this sequential explanatory mixed-methods study examines the predictive effect of perceived supports on learners’ engagement in chatbot-assisted language learning, and the mediating role of basic psychological needs (BPNs) in the relationship. Data from 506 Chinese EFL learners (quantitative phase: structural equation modeling of survey responses) and 18 follow-up interviewees (qualitative phase: thematic analysis) indicated that: (1) perceived supports had a significant and positive impact on cognitive and emotional engagement, as opposed to behavioral and agentic engagement; (2) perceived supports significantly and positively predicted BPNs (autonomy, competence, and relatedness); (3) BPNs significantly and positively influenced behavioral, cognitive, emotional, and agentic engagement; and (4) perceived supports indirectly affected emotional and agentic engagement through autonomy, and behavioral engagement through relatedness, while competence consistently mediated all four engagement dimensions. Furthermore, qualitative insights from the semi-structured interviews triangulated the quantitative results, offering a comprehensive understanding of EFL learners’ engagement. The findings shed light on the future research avenues and the practical deployment of GenAI chatbots in EFL education.