AI-driven language learning in higher education: an empirical study on self-reflection, creativity, anxiety, and emotional resilience in EFL learners

Min He (), Babar Nawaz Abbasi and Jinhua He
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Min He: Shaanxi Normal University
Babar Nawaz Abbasi: Hangzhou Normal University
Jinhua He: Southwest Jiaotong University

Humanities and Social Sciences Communications, 2025, vol. 12, issue 1, 1-20

Abstract: Abstract In modern times, artificial intelligence (AI) technologies are reshaping traditional methods of language learning in higher education. However, their impact on language learners’ self-reflection, creativity, anxiety reduction, and emotional resilience has not been empirically explored in depth. Therefore, this study aims to fill that gap by examining English as a Foreign Language (EFL) learners using Structural Equation Modeling (SEM), Quantile Regression (QR), and Phenomenological Analysis (PA), using a survey of 205 EFL undergraduate learners through semi-structured and structured questionnaires from various Chinese universities. The results revealed that corrective AI-powered feedback, such as grammar and vocabulary corrections, along with motivational feedback, including encouragement and progress tracking, significantly improved EFL learners’ self-reflection. Furthermore, creativity and AI-powered feedback encouraged EFL learners to become more creative in their use of English, more confident in expressing original ideas, and increased enjoyment in writing and speaking. Moreover, familiarity with AI-powered feedback and the manner in which EFL learners receive this feedback have partially reduced their performance anxiety. In addition, AI-powered feedback is enhancing emotional resilience by making them more confident in overcoming setbacks. However, the study offers policy suggestions for both government and AI vendors/companies.

Date: 2025
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DOI: 10.1057/s41599-025-05817-5

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