Exploring AI in English medium instruction through Q-methodology analysis

This study explores the transformative role of artificial intelligence (AI) in addressing challenges in English Medium Instruction (EMI) in higher education in Japan, such as limited teacher availability and diverse English proficiency levels among students. By incorporating AI tools like ChatGPT, DeepL, and Grammarly into an EMI course, the research examines AI’s potential to enhance student engagement, collaboration, and bridge language proficiency gaps. Using Q-methodology to capture subjective viewpoints, the study involved 32 Japanese students. Participants sorted 16 statements about AI’s role in education on a scale from -3 to +3. Principal component analysis (PCA) revealed three principal components explaining 72% of the variance: The Isolated User: This group acknowledges AI’s benefits for critical thinking and personalization but expresses concerns about over-reliance and reduced engagement. The Collaborator: This perspective emphasizes AI's role in enhancing active learning and addressing diverse needs while noting challenges in cultural sensitivity and personalization. The Optimist Challenger: This viewpoint is largely positive, seeing AI as crucial for boosting engagement and preparing students for global markets, though it also highlights concerns about AI replacing traditional teaching methods and increasing technological divides. The study concludes that AI can significantly enhance learning experiences by personalizing education and overcoming language barriers. However, potential drawbacks include student dependency on technology, reduced engagement, and cultural insensitivity. Effective AI integration in EMI necessitates robust training for both students and teachers and ongoing research to refine AI applications. Future research should focus on longitudinal studies to monitor AI’s impact and conduct additional Q-sorts to capture changes in student attitudes, providing deeper insights into the effectiveness of educational strategies.

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Publication Details

Journal
Proceedings of the International CALL Research Conference
Published
2024-09-21
DOI
https://doi.org/10.29140/9780648184485-15
Citations
2
Primary Topic
Q Methodology Applications
Type
article
Field-Weighted Citation Impact
0.98
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article

Exploring AI in English medium instruction through Q-methodology analysis

Hisayo Kikuchi
2 citations
Proceedings of the International CALL Research Conference
Q Methodology Applications
0.98
article

Exploring AI in English medium instruction through Q-methodology analysis

Hisayo Kikuchi
article en
2 citations

Abstract

This study explores the transformative role of artificial intelligence (AI) in addressing challenges in English Medium Instruction (EMI) in higher education in Japan, such as limited teacher availability and diverse English proficiency levels among students. By incorporating AI tools like ChatGPT, DeepL, and Grammarly into an EMI course, the research examines AI’s potential to enhance student engagement, collaboration, and bridge language proficiency gaps. Using Q-methodology to capture subjective viewpoints, the study involved 32 Japanese students. Participants sorted 16 statements about AI’s role in education on a scale from -3 to +3. Principal component analysis (PCA) revealed three principal components explaining 72% of the variance: The Isolated User: This group acknowledges AI’s benefits for critical thinking and personalization but expresses concerns about over-reliance and reduced engagement. The Collaborator: This perspective emphasizes AI's role in enhancing active learning and addressing diverse needs while noting challenges in cultural sensitivity and personalization. The Optimist Challenger: This viewpoint is largely positive, seeing AI as crucial for boosting engagement and preparing students for global markets, though it also highlights concerns about AI replacing traditional teaching methods and increasing technological divides. The study concludes that AI can significantly enhance learning experiences by personalizing education and overcoming language barriers. However, potential drawbacks include student dependency on technology, reduced engagement, and cultural insensitivity. Effective AI integration in EMI necessitates robust training for both students and teachers and ongoing research to refine AI applications. Future research should focus on longitudinal studies to monitor AI’s impact and conduct additional Q-sorts to capture changes in student attitudes, providing deeper insights into the effectiveness of educational strategies.

Proceedings of the International CALL Research ConferenceVol. 2024
Aoyama Gakuin University (JP)
Openalex Percentile: Top 19%
Q Methodology Applications
0.98
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