Associations Between Generative AI–Based Pronunciation Feedback and Willingness to Communicate in English: The Mediating Role of English Pronunciation Self-Efficacy

Synthesis: Lu et al. (2026) examined, through the lens of Social Cognitive Theory, whether Chinese university EFL learners' perceptions of generative-AI-based pronunciation feedback relate to their willingness to communicate (WTC) in English, with English pronunciation self-efficacy as a hypothesized mediator. Using a cross-sectional survey of 1,701 learners, covariance-based structural equation modeling, and bias-corrected bootstrapping, they found that positive perceptions of GenAI pronunciation feedback were significantly associated with greater WTC in English, and that pronunciation self-efficacy partially mediated this relationship — the indirect effect accounted for 69.9% of the total effect while the direct effect remained significant.

Key Findings

Study Design & Method

The authors used a cross-sectional survey design with a convenience sample of 1,701 Chinese university EFL learners. Covariance-based structural equation modeling (CB-SEM) and bias-corrected bootstrapping were used to test the hypothesized relationships and the mediating effect of pronunciation self-efficacy on the perception-of-GenAI-feedback → WTC path. The analysis distinguished the indirect effect (through self-efficacy) from the direct effect of feedback perception on willingness to communicate, and quantified the indirect effect's share of the total effect.

Implications for AI in Education

The findings position GenAI-based pronunciation feedback as a promising, low-pressure supplement to traditional teacher and peer feedback for Language Learning, particularly in contexts (like China) where limited authentic English interaction and "mute English" make pronunciation anxiety a barrier to speaking. Because GenAI feedback is immediate, repeatable, personalized, and perceived as less judgmental, it may build learners' pronunciation self-efficacy and, through it, their readiness to speak. For practitioners, this supports integrating AI pronunciation tools as ongoing speaking practice that complements — not replaces — classroom feedback, while the partial mediation highlights that confidence and psychological safety, not just accuracy, are central to why such feedback helps learners communicate.

Limitations

The cross-sectional design precludes causal inference, and the convenience sample of Chinese university EFL learners bounds generalizability to other populations and contexts. All constructs (perceptions of feedback, pronunciation self-efficacy, WTC) are self-report measures collected at a single time point, so the mediating role of self-efficacy is inferred from covariance rather than manipulated. The study focuses on perceptions of GenAI feedback rather than objective feedback quality or its actual behavioral effects on speaking performance.

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Citation

Lu, Y., Yang, Y., Cui, T., Yang, Z., Cai, Y., & Jing, B. (2026). Associations between generative AI–based pronunciation feedback and willingness to communicate in English: The mediating role of English pronunciation self-efficacy.