Mixed methods reveal ASR enhances linguistic accuracy in English learners, suggesting tailored approaches based on device type and learning styles.
Abstract This study explores the impact of Automatic Speech Recognition (ASR) technology on English language learning among non-native speakers. We specifically investigate the roles of learners’ geographic and cultural backgrounds, device type (high end Apple and Android devices), and learning styles (visual, auditory) in mediating the efficacy of ASR. Employing a mixed methods approach, our research integrates traditional teaching methods with ASR's real-time feedback to assess its effectiveness across different learner profiles in a controlled environment. Results indicate that while ASR technology significantly enhances linguistic accuracy and fluency, its success varies significantly depending on the learner's origin, the sophistication of the technology used, and the learner's preferred sensory modalities. Particularly, visual learners show the most substantial gains, suggesting thatASR technology integration should be tailored to individual learning styles to maximize educational outcomes. This study highlights the critical need for adaptive learning environments that align with diverse learner needs, providing key insights for educators and developers aiming to optimize technology enhanced language learning platforms.
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Fan Yuan (2025) studied this question.
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