How LLM Chatbots Shape Self‐Regulated Language Learning: The Interplay of Cognitive Load and Basic Psychological Needs

ABSTRACT Background The integration of large language model (LLM) chatbots into language education has opened new opportunities for supporting self‐regulated language learning (SRLL). However, their effectiveness depends on how cognitive load and basic psychological needs (BPNs) jointly shape learners' self‐regulation. Objectives This study proposes an exploratory model to elucidate the potential pathways by which extraneous and germane cognitive load (ECL, GCL) and the satisfaction of BPNs (autonomy, competence, and relatedness) jointly influence SRLL in chatbot‐assisted environments. Methods A two‐stage explanatory mixed‐methods design was employed. Quantitative data were collected from 237 English as a foreign language (EFL) learners and analysed using partial least squares structural equation modelling (PLS‐SEM). Qualitative data from semi‐structured interviews were thematically analysed to explain and triangulate the quantitative findings. Results and Conclusions ECL generated by chatbots' technological limitations did not directly undermine SRLL but significantly frustrated perceived competence (PC), which in turn discouraged self‐regulation. Nevertheless, these negative effects were limited, as most learners remained willing to engage with LLM chatbots. In contrast, BPN satisfaction emerged as a strong motivator of SRLL behaviours: PC and perceived relatedness (PR) directly promoted SRLL, while perceived autonomy (PA) and PC indirectly facilitated SRLL by enhancing GCL, which also exerted a direct, significant influence on SRLL. These findings highlight the dual role of LLM chatbots: While their limitations may induce tolerable ECL and competence frustration, their affordances in satisfying BPNs and fostering GCL make them a promising tool for sustaining SRLL in EFL contexts.