How AI shapes self-regulated learning in foreign language education

With the rapid integration of Artificial Intelligence (AI) into education, how learners engage in self-regulated learning (SRL) in AI-supported environments has become a key issue in foreign language education research. Grounded in Zimmerman’s (in: Boekaerts, Pintrich, Zeidner (eds) Handbook of self-regulation, Academic Press, 2000; Theory Into Practice 41(2):64–70, 2002) theory of SRL, this study explores the structural dimensions and underlying mechanisms of Chinese EFL learners’ SRL in AI-empowered contexts. A mixed-methods design was employed, combining quantitative data from 180 questionnaire responses with qualitative data from semi-structured interviews with 14 learners. Through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), this study identified and validated the latent dimensions of AI-supported SRL and examined their interrelationships. The results revealed that SRL in AI-supported environments comprises three phases: Forethought phase—motivational beliefs and learning planning; Performance phase—learning process and strategy execution; and Self-reflection phase—reflection and technological adaptability. SEM analysis showed that the forethought phase had a significant positive effect on the performance phase and a direct effect on the self-reflection phase, whereas the path from performance phase to self-reflection phase was not significant. Moreover, a significant positive correlation was found between the self-reflection phase and forethought phase, revealing a continuous “reflection–forethought” cyclical mechanism. Theoretically, this study extends Zimmerman’s (in: Boekaerts, Pintrich, Zeidner (eds) Handbook of self-regulation, Academic Press, 2000; Theory Into Practice 41(2):64–70, 2002) SRL model by highlighting new features of human-AI co-regulation in learning processes and provides empirical evidence to inform the pedagogical optimization of AI integration in foreign language education.

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Publication Details

Journal
Discover Computing
Published
2025-12-31
DOI
https://doi.org/10.1007/s10791-025-09801-0
Primary Topic
Innovative Teaching and Learning Methods
Type
article
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article

How AI shapes self-regulated learning in foreign language education

Changyin Zhou, Fanfan Hou
Discover Computing
Innovative Teaching and Learning Methods
article

How AI shapes self-regulated learning in foreign language education

Changyin Zhou, Fanfan Hou
article en

Abstract

With the rapid integration of Artificial Intelligence (AI) into education, how learners engage in self-regulated learning (SRL) in AI-supported environments has become a key issue in foreign language education research. Grounded in Zimmerman’s (in: Boekaerts, Pintrich, Zeidner (eds) Handbook of self-regulation, Academic Press, 2000; Theory Into Practice 41(2):64–70, 2002) theory of SRL, this study explores the structural dimensions and underlying mechanisms of Chinese EFL learners’ SRL in AI-empowered contexts. A mixed-methods design was employed, combining quantitative data from 180 questionnaire responses with qualitative data from semi-structured interviews with 14 learners. Through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), this study identified and validated the latent dimensions of AI-supported SRL and examined their interrelationships. The results revealed that SRL in AI-supported environments comprises three phases: Forethought phase—motivational beliefs and learning planning; Performance phase—learning process and strategy execution; and Self-reflection phase—reflection and technological adaptability. SEM analysis showed that the forethought phase had a significant positive effect on the performance phase and a direct effect on the self-reflection phase, whereas the path from performance phase to self-reflection phase was not significant. Moreover, a significant positive correlation was found between the self-reflection phase and forethought phase, revealing a continuous “reflection–forethought” cyclical mechanism. Theoretically, this study extends Zimmerman’s (in: Boekaerts, Pintrich, Zeidner (eds) Handbook of self-regulation, Academic Press, 2000; Theory Into Practice 41(2):64–70, 2002) SRL model by highlighting new features of human-AI co-regulation in learning processes and provides empirical evidence to inform the pedagogical optimization of AI integration in foreign language education.

Discover ComputingVol. 28(1)
Beijing International Studies University (CN)
Quality Education
Openalex Percentile: Top 25%
Innovative Teaching and Learning Methods
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