Artificial intelligence in language learning: biometric feedback and adaptive reading for improved comprehension and reduced anxiety

Hang Yuan ()
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Hang Yuan: Zhengzhou Shengda University of Economic Business & Management

Palgrave Communications, 2025, vol. 12, issue 1, 1-16

Abstract: Abstract This study investigates the efficacy of an AI-enhanced reading platform in enhancing reading comprehension and influencing psychological variables such as motivation, anxiety, and cognitive load among second language (L2) learners. A mixed-methods approach was employed, combining quantitative analyses of reading comprehension scores and psychological measures with qualitative exploration through focus group discussions. A total of 300 participants were randomly assigned to either the experimental group, which used the AI platform, or the control group, which used traditional digital resources. Quantitative results revealed significant improvements in reading comprehension, motivation, anxiety reduction, and cognitive load management among participants using the AI platform compared to the control group. Effect sizes indicated large to moderate impacts of the intervention across all measured variables. Biometric data analysis, including heart rate variability (HRV) and eye-tracking metrics, provided objective evidence supporting the platform’s efficacy in reducing stress levels and promoting more efficient reading behaviors. Qualitative findings illuminated participants’ experiences, highlighting enhanced engagement, reduced anxiety, development of improved reading strategies, and appreciation for the user-friendly platform design. The study underscores the potential of AI-driven educational technologies to optimize language learning outcomes by providing personalized, adaptive, and supportive learning environments.

Date: 2025
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DOI: 10.1057/s41599-025-04878-w

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