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<p>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.</p>

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How AI shapes self-regulated learning in foreign language education

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月31日

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  • Fanfan Hou,
  • Changyin Zhou

摘要

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.