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<p>Artificial Intelligence (AI) has introduced a fundamental transformation in EFL (English as a Foreign Language) learning and demonstrated considerable potential to improve educational outcomes. However, its application remains underexplored. This study explores the interplay of academic buoyancy (AB), academic motivation (AM), academic performance (AP), and AI readiness (AIR) among Chinese college students in their EFL learning. Despite the growing research interest in these constructs, their combined impact on EFL learning has not been systematically studied. Utilising data from 447 students and employing Structural Equation Modelling (SEM), the study reveals that AB positively influences AM, AP, and AIR. AM significantly affects AP but does not significantly affect AIR, while AP positively influences AIR. Gender directly influences AIR but does not moderate the relationships between AB, AM, AP, and AIR. Educational levels neither significantly affect AIR nor moderate the relationships among the other constructs. The study concludes that fostering learners’ AB and AM may effectively improve their AIR. The findings suggest that educational strategies should focus on building students’ resilience and enhancing their motivation to effectively integrate AI into EFL education. Future research is expected to further verify these findings of the study through dynamic and longitudinal tracking as well as comparative analysis across various educational contexts.</p>

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AI-transforming EFL learning: investigating the interplay of academic buoyancy, motivation, performance and AI readiness among Chinese college students

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月11日

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  • Xiaolan Wen,
  • Dan Jin

摘要

Artificial Intelligence (AI) has introduced a fundamental transformation in EFL (English as a Foreign Language) learning and demonstrated considerable potential to improve educational outcomes. However, its application remains underexplored. This study explores the interplay of academic buoyancy (AB), academic motivation (AM), academic performance (AP), and AI readiness (AIR) among Chinese college students in their EFL learning. Despite the growing research interest in these constructs, their combined impact on EFL learning has not been systematically studied. Utilising data from 447 students and employing Structural Equation Modelling (SEM), the study reveals that AB positively influences AM, AP, and AIR. AM significantly affects AP but does not significantly affect AIR, while AP positively influences AIR. Gender directly influences AIR but does not moderate the relationships between AB, AM, AP, and AIR. Educational levels neither significantly affect AIR nor moderate the relationships among the other constructs. The study concludes that fostering learners’ AB and AM may effectively improve their AIR. The findings suggest that educational strategies should focus on building students’ resilience and enhancing their motivation to effectively integrate AI into EFL education. Future research is expected to further verify these findings of the study through dynamic and longitudinal tracking as well as comparative analysis across various educational contexts.