AI as a Catalyst for Learner-Centred English Education in Rural Japan: Early Classroom Observations



Author Information

Eurong, Aaron Lim, University of St Andrews, United Kingdom

Abstract

In rural Japanese high schools, English education is often characterised by teacher-centred, grammar-translation approaches, reflecting hierarchical classroom cultures and limited exposure to alternative pedagogical methods. At the same time, the integration of artificial intelligence (AI) tools, such as DeepL and Copilot, presents new possibilities for student autonomy and engagement, particularly in rural contexts where access to global content is otherwise constrained. This exploratory poster outlines the initial conceptualisation of a study, pending school approval, investigating how limited and curated AI exposure might serve as a catalyst for shifting English pedagogy toward more humanist, learner-centred approaches. Preliminary observations and literature suggest that AI could enable students to access global perspectives independently, experiment with diverse learning strategies, and engage in self-directed language practice, fostering their emerging sense of global citizenship. Alongside these opportunities, reliance on AI may influence social interaction, peer collaboration, and the teacher-student dynamic, reflecting tensions between innovation and cultural norms. Ethical considerations, including voluntary participation, parental consent, and anonymisation, will be followed once approval is granted. By highlighting both potential opportunities and challenges, and situating the research within the distinctive constraints of rural Japanese schools, this work seeks to stimulate discussion on how AI could support pedagogical reform while balancing learner autonomy, human interaction, and cultural context. The poster aims to engage educators and researchers in dialogue about integrating AI into English language classrooms in ways that respect both educational tradition and global engagement.


Paper Information

Conference: ACSS2026
Stream: Teaching and Learning

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Posted by James Alexander Gordon