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

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Title: How LLM Chatbots Shape Self-Regulated Language Learning: The Interplay of Cognitive Load and Basic Psychological Needs
Language: English
Authors: Chun Hao (ORCID 0009-0006-3310-7527), Ning Ma, Mohd Rashid Bin Mohd Saad, Huzaina Binti Abdul Halim, Mengmeng Guo, Mengyao Hao
Source: Journal of Computer Assisted Learning. 2026 42(1).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 16
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Descriptors: Natural Language Processing, Artificial Intelligence, Second Language Learning, English (Second Language), Cognitive Processes, Difficulty Level, Computer Mediated Communication, Psychological Needs, Self Management
DOI: 10.1002/jcal.70163
ISSN: 0266-4909
1365-2729
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.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1495718
Database: ERIC
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  Data: How LLM Chatbots Shape Self-Regulated Language Learning: The Interplay of Cognitive Load and Basic Psychological Needs
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  Data: <searchLink fieldCode="AR" term="%22Chun+Hao%22">Chun Hao</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0006-3310-7527">0009-0006-3310-7527</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ning+Ma%22">Ning Ma</searchLink><br /><searchLink fieldCode="AR" term="%22Mohd+Rashid+Bin+Mohd+Saad%22">Mohd Rashid Bin Mohd Saad</searchLink><br /><searchLink fieldCode="AR" term="%22Huzaina+Binti+Abdul+Halim%22">Huzaina Binti Abdul Halim</searchLink><br /><searchLink fieldCode="AR" term="%22Mengmeng+Guo%22">Mengmeng Guo</searchLink><br /><searchLink fieldCode="AR" term="%22Mengyao+Hao%22">Mengyao Hao</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Computer+Assisted+Learning%22"><i>Journal of Computer Assisted Learning</i></searchLink>. 2026 42(1).
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: 16
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Natural+Language+Processing%22">Natural Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Learning%22">Second Language Learning</searchLink><br /><searchLink fieldCode="DE" term="%22English+%28Second+Language%29%22">English (Second Language)</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Processes%22">Cognitive Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Difficulty+Level%22">Difficulty Level</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Mediated+Communication%22">Computer Mediated Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+Needs%22">Psychological Needs</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Management%22">Self Management</searchLink>
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  Data: 10.1002/jcal.70163
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  Data: 0266-4909<br />1365-2729
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  Data: 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.
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