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Integrating large language models into EFL writing instruction: effects on performance, self-regulated learning strategies, and motivation.

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Title: Integrating large language models into EFL writing instruction: effects on performance, self-regulated learning strategies, and motivation.
Authors: Liu, Ze-Min1 (AUTHOR), Hwang, Gwo-Jen2 (AUTHOR), Chen, Chuang-Qi3 (AUTHOR), Chen, Xiang-Dong4 (AUTHOR), Ye, Xin-Dong3 (AUTHOR) yxd@wzu.edu.cn
Source: Computer Assisted Language Learning. May2026, Vol. 39 Issue 3, p466-490. 25p.
Subject Terms: *Self-regulated learning, *Foreign language education, *Intrinsic motivation, *Elementary education, *Intelligent tutoring systems, Language models, Participation
Abstract: This study aimed to investigate the efficacy of utilizing large language models (LLMs) to enhance self-regulated learning (SRL) strategy instruction in English as a Foreign Language (EFL) writing. An LLM-supported Cognitive Academic Language Learning Model (CALLA-LLM) was developed and examined for its potential to improve elementary students' EFL writing performance, SRL strategy use, and writing motivation. In a randomized controlled trial, 65 elementary school students were divided into an experimental group receiving CALLA-LLM instruction and a control group receiving traditional CALLA instruction. Both groups learned SRL strategies over 5 weeks, with data collected pre-intervention, post-intervention, and at a one-month follow-up. Results showed that the CALLA-LLM group made significant improvements in writing performance, SRL strategy use, and writing motivation, maintained most of the gains at follow-up, and significantly outperformed the control group. Findings provide empirical evidence for the efficacy of the CALLA-LLM model in enhancing EFL writing strategy instruction, lending support for integrating AI technologies such as LLMs into English language teaching. Moreover, the study underscores the importance of the "Humans in the Loop" approach, which emphasizes the essential role of human educators in AI-assisted language instruction. [ABSTRACT FROM AUTHOR]
Copyright of Computer Assisted Language Learning is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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Header DbId: ehh
DbLabel: Education Research Complete
An: 193318274
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
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  Data: Integrating large language models into EFL writing instruction: effects on performance, self-regulated learning strategies, and motivation.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Ze-Min%22">Liu, Ze-Min</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hwang%2C+Gwo-Jen%22">Hwang, Gwo-Jen</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Chuang-Qi%22">Chen, Chuang-Qi</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Xiang-Dong%22">Chen, Xiang-Dong</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ye%2C+Xin-Dong%22">Ye, Xin-Dong</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> yxd@wzu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Computer+Assisted+Language+Learning%22">Computer Assisted Language Learning</searchLink>. May2026, Vol. 39 Issue 3, p466-490. 25p.
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  Data: *<searchLink fieldCode="DE" term="%22Self-regulated+learning%22">Self-regulated learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Foreign+language+education%22">Foreign language education</searchLink><br />*<searchLink fieldCode="DE" term="%22Intrinsic+motivation%22">Intrinsic motivation</searchLink><br />*<searchLink fieldCode="DE" term="%22Elementary+education%22">Elementary education</searchLink><br />*<searchLink fieldCode="DE" term="%22Intelligent+tutoring+systems%22">Intelligent tutoring systems</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Participation%22">Participation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study aimed to investigate the efficacy of utilizing large language models (LLMs) to enhance self-regulated learning (SRL) strategy instruction in English as a Foreign Language (EFL) writing. An LLM-supported Cognitive Academic Language Learning Model (CALLA-LLM) was developed and examined for its potential to improve elementary students' EFL writing performance, SRL strategy use, and writing motivation. In a randomized controlled trial, 65 elementary school students were divided into an experimental group receiving CALLA-LLM instruction and a control group receiving traditional CALLA instruction. Both groups learned SRL strategies over 5 weeks, with data collected pre-intervention, post-intervention, and at a one-month follow-up. Results showed that the CALLA-LLM group made significant improvements in writing performance, SRL strategy use, and writing motivation, maintained most of the gains at follow-up, and significantly outperformed the control group. Findings provide empirical evidence for the efficacy of the CALLA-LLM model in enhancing EFL writing strategy instruction, lending support for integrating AI technologies such as LLMs into English language teaching. Moreover, the study underscores the importance of the "Humans in the Loop" approach, which emphasizes the essential role of human educators in AI-assisted language instruction. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computer Assisted Language Learning is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/09588221.2024.2389923
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      – Code: eng
        Text: English
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        PageCount: 25
        StartPage: 466
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      – SubjectFull: Self-regulated learning
        Type: general
      – SubjectFull: Foreign language education
        Type: general
      – SubjectFull: Intrinsic motivation
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      – SubjectFull: Elementary education
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      – SubjectFull: Intelligent tutoring systems
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      – SubjectFull: Language models
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      – SubjectFull: Participation
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      – TitleFull: Integrating large language models into EFL writing instruction: effects on performance, self-regulated learning strategies, and motivation.
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            NameFull: Liu, Ze-Min
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            NameFull: Chen, Chuang-Qi
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            NameFull: Chen, Xiang-Dong
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            – D: 01
              M: 05
              Text: May2026
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              Y: 2026
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