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Description
Abstract
Generative artificial intelligence (AI) tools are rapidly reshaping language education, requiring educators to balance technological integration with pedagogical responsibility. Grounded in Priestley et al.’s (2015) ecological perspective of teacher agency, this study aims to examine how participation in AI-empowered corpus-based language pedagogy (CBLP) competitions shapes the agency of English language teachers.
This mixed-methods case study tracked five Vietnamese teachers and student teachers in CBLP lesson design and teaching competitions organised by The Education University of Hong Kong. Data were collected through pre- and post-competition questionnaires and semi-structured interviews.
Quantitative findings reveal positive shifts in participants’ readiness and confidence to implement AI-empowered CBLP integration. Rather than acting as passive consumers of technology, participants actively exercised teacher agency by critically evaluating and adapting AI-generated materials to ensure pedagogical alignment with specific learner needs and local contexts. Qualitative data highlight a distinct transition from intuition-based teaching to a more systematic and data-driven approach, where corpus evidence served as an empirical baseline to verify AI suggestions. Teacher agency was significantly fostered through collaborative reflection, empowering participants to navigate technical challenges and refine instructional strategies based on expert and peer feedback.
These results position AI and corpus tools as supports, not substitutes, for educator expertise and judgement, thereby deepening the theoretical understanding of teacher agency during the current AI evolution in language education and offering practical insights for designing evidence-based TESOL professional development initiatives.
Keywords: generative artificial intelligence, corpus-based language pedagogy, tesol, teacher agency, professional development
References
Priestley, M., Biesta, G., & Robinson, S. (2015). Teacher agency: An ecological approach. Bloomsbury Academic. https://doi.org/10.5040/9781474219426