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October 12, 2025Porta Linguarum Revista Interuniversitaria de Didáctica de las Lenguas Extranjeras23 citationsOpen Access

Exploring Chinese EFL learners’ beliefs about AI-mediated informal digital learning of English: Insights from Q Methodology

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XWXiaochen WangYGYang GaoBRBarry Lee Reynolds

Key Points

  • Three main belief types about AI in learning emerged: optimistic, critical, and hesitant, reflecting varied learner views.
  • Among 20 Chinese EFL learners, 30% held optimistic beliefs about AI's role in enhancing learning efficiency and engagement.
  • This research utilized Q methodology to investigate learner beliefs, highlighting the need for tailored AI educational tools.
  • Understanding these beliefs is crucial for developing AI-based tools that effectively meet diverse learner needs and preferences.

Abstract

As technology becomes increasingly integrated into language learning, AI has emerged as a promising tool for enhancing personalized and engaging experiences. However, research on its role in informal digital learning, particularly through learners’ perspectives, remains limited. This study used Q methodology, with a sample of 20 Chinese EFL learners, to explore their beliefs about AI-mediated informal digital learning, identifying three main belief types. Results reported three primary types of beliefs: optimistic AI beliefs, critical AI beliefs, and hesitant AI beliefs. Optimistic AI beliefs reflect learners who view AI as a revolutionary tool that boosts learning efficiency and engagement, showing enthusiasm for new AI applications in language learning. Critical AI beliefs characterize learners who recognize AI’s benefits but remain cautious, critically assessing its limitations and potential drawbacks. Hesitant AI beliefs describe learners who, while acknowledging AI’s potential, harbor doubts about its overall effectiveness in informal English learning. By shedding light on learners' varied beliefs about AI in informal language learning, the study this study contributes to a deeper understanding of how learners perceive and engage with AI-powered language learning tools. These findings have significant implications for the design and development of more effective and personalized AI-based educational tools that cater to diverse learner needs and preferences.

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Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/68ebffcfdef9fcb308ff2430https://doi.org/10.30827/portalin.vixiii.31925
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