Integrating large language models into EFL writing instruction: effects on performance, self-regulated learning strategies, and motivation
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.
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Positive outcomes on writing performance, self-regulated learning and motivation have been reported when LLMs are systematically integrated into instruction.
“In the realm of early intervention, positive outcomes on writing performance [5], [6], self-regulated learning and motivation [6] have been reported when LLMs are systematically integrated into instruction”
Complementary studies demonstrated performance to vary as a function of model and prompt when using LLMs to evaluate ELL writing, non-native Japanese writing, and LLMs in general for automated writing evaluation.
“Complementary studies demonstrated performance to vary as a function of model and prompt when using LLMs to evaluate ELL writing [9], non-native Japanese writing [16], and LLMs in general for automated writing evaluation [5].”
Studies used AI interventions to address common barriers such as limited teacher feedback, low student motivation for revision, or insufficient opportunities for creative expression.
“These studies used AI interventions to address common barriers such as limited teacher feedback, low student motivation for revision, or insufficient opportunities for creative expression [81-87].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [82]
Across writing, reading, and mathematics tasks, students repeatedly submit work to AI systems, receive concrete suggestions, and resubmit revised work, creating a cycle of practice, feedback, and renewed practice.
“Across writing, reading, and mathematics tasks, students repeatedly submit texts, assignments, or answers to AI systems, receive concrete suggestions on error correction, structure, and language use, and then resubmit revised work, creating a cycle of practice, feedback, and renewed practice [75,77,82,83,87,90,110].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [82]
Motivation gains are not guaranteed: several studies find no significant effects or only short-lived novelty bumps that taper over time.
“Motivation gains are not guaranteed: several studies find no significant effects [109] or only short-lived novelty bumps that taper over time [82,122].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [82]
Teacher presence and guidance, together with personalization in AI tool design, are key enabling conditions.
“Teacher presence and guidance [82,107], together with personalization in AI tool design [97], are key enabling conditions.”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [82]
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- Authors
- Liu, Z.-M., Hwang, G.-J., Chen, C.-Q., Chen, X.-D., & Ye, X.-D.
- Affiliations
- East China Normal University (CN)
- National Taichung University of Education (TW)
- Department of Educational Technology, Wenzhou University
- Faculty of Education, East China Normal University
- Venue
- Computer Assisted Language Learning
- Publisher
- Informa UK Limited
- Published
- 2024-08-12
- Volume
- 39
- Issue
- 3
- Pages
- 466-490
- ISSN
- 0958-8221 (ISSN-L)
- 1744-3210
- OpenAlex
- W4401515033
- Cited by
- 115
- References
- 55
- Citations by year
- 2026: 55 · 2025: 57 · 2024: 3
- FWCI
- 181.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Assisted Language Learning (publishedVersion)
- Funders
- National Social Science Fund of China (BCA190089)
- Topics
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Physical Sciences › Computer Science › Information Systems › English Language Learning and Teaching
- Social Sciences › Psychology › Developmental and Educational Psychology › Second Language Acquisition and Learning
- Keywords
- large language models, EFL writing instruction, self-regulated learning strategies, writing motivation, Cognitive Academic Language Learning Approach, elementary school students, AI-assisted language learning, human-in-the-loop, writing performance
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