International Journal of
Information and Education Technology

Abstract—As English-Medium Instruction (EMI) becomes increasingly prevalent in non-English-speaking higher education, language-related challenges continue to affect both students and instructors. This study explores how AI-assisted technologies, Text-to-Speech (TTS) and voice cloning, can enhance instructional clarity and learner engagement in a STEM EMI course at a Korean university. Drawing on a mixed-methods design, survey responses from 27 undergraduate students and interview data from the course instructor were analyzed to investigate perceptions of Artificial Intelligence (AI)-generated English voice lectures. Factor analysis identified three key dimensions: EMI-related learning challenges, satisfaction with English delivery, and perceived usefulness of AI narrated videos. Qualitative findings highlighted the affordances of standardized pronunciation, replayability, and multimodal delivery in reducing cognitive load. The study also uncovered pedagogical considerations, including tensions between linguistic accessibility and EMI’s instructional goals. The findings suggest that AI voice tools can offer meaningful support in EMI, but require careful integration aligned with institutional goals and ethical guidelines.

Keywords—English-Medium Instruction (EMI), Text-to-Speech (TTS), voice cloning, instructional videos

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Cite: Yun-Sun Shin and Yoo Young Ahn, "AI-Assisted Learning in EMI: A Case Study of Leveraging TTS and Voice Cloning in a Korean EMI Course," International Journal of Information and Education Technology, vol. 15, no. 11, pp. 2366-2375, 2025.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).