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p-Index From 2021 - 2026
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All Journal Journal of English Language Teaching and Linguistics
Takayoshi Sako
Department of Education, Meisei University
Education

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Enhancing Critical Thinking through AI-Assisted Collaborative Task-Based Learning: A Case Study of Prospective Teachers in Japan Takayoshi Sako
Journal of English Language Teaching and Linguistics Journal of English Language Teaching and Linguistics, 9(2), August 2024
Publisher : Yayasan Visi Intan Permata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21462/jeltl.v9i2.1319

Abstract

This study explores the potential of integrating generative AI into collaborative task-based language learning to foster critical thinking (CT) skills in computer-assisted learning environments. Employing a mixed-methods approach, the research examines the experiences of 18 pre-service teachers engaging in AI-assisted collaborative activities. Quantitative analysis of the Critical Thinking Disposition Scale and qualitative analysis using the modified grounded theory approach (M-GTA) reveal that AI tools can alleviate collaboration challenges by bridging perceptual gaps, clarifying objectives, and promoting deeper understanding. The findings suggest that AI-assisted collaborative learning enhances students’ intellectual autonomy, creativity, and digital literacy skills. However, the effectiveness of AI tools in fostering collaboration depends on their proper application, the supportive role of educators, and the careful design of evaluation criteria. The study emphasizes the importance of balancing the use of AI tools with authentic language production and highlights the crucial role of educators in moderating discussions and providing guidance where AI tools may have limitations. While the findings offer valuable insights, the study acknowledges its limitations and recommends future research to explore the impact of AI-assisted collaborative learning on CT with larger and more diverse samples.
Fostering Interactional Competence through AI-Based Virtual Dialogue: Task Awareness Transformation in Japanese Pre-service Elementary School Teachers Takayoshi Sako; Naoyuki Kiryu
Journal of English Language Teaching and Linguistics Journal of English Language Teaching and Linguistics, 11(1), April 2026
Publisher : Yayasan Visi Intan Permata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21462/jeltl.v11i1.1929

Abstract

Developing interactional competence (IC) remains a significant challenge in Japanese elementary English teacher education, where pre-service teachers tend to prioritize linguistic accuracy over pedagogical facilitation. This pilot study investigates how a generative AI-based virtual dialogue environment—one specifically designed to introduce “friction” through unpredictable and incomplete responses—may reshape pre-service teachers' task awareness. Two university students took part in a two-week intervention using ChatGPT’s voice mode, assuming the role of teachers interacting with an AI “child.” Semi-structured interview data were analyzed using a Grounded Theory Approach (GTA). The analysis reveals a four-stage process: (1) initial difficulty arising from the gap between expectations and reality, (2) a shift in awareness from self-oriented linguistic anxiety toward learner-focused facilitation, (3) the concretization of perceived classroom conflicts, and (4) an emerging desire for pedagogical support. A central finding is that AI-mediated “friction” serves as a productive catalyst for professional learning, destabilizing existing frames of reference and prompting participants to redefine IC as the co-construction of meaning. The study carries several implications for teacher education and AI design. It proposes the principle of “intentional imperfection” in AI behavior, suggesting that rather than providing flawless models, AI for teacher training should generate manageable interactional trouble to elicit pedagogical judgment. The findings also highlight the necessity of embedding such simulations within a scaffolded framework that provides diagnostic feedback and links virtual practice to real-world professional vision. Together, these contributions offer a conceptual foundation for utilizing AI to prepare teachers for the interactional complexities of the language classroom.
Co-Authors Naoyuki Kiryu