Enhancing Critical Thinking through AI-Assisted Collaborative Task-Based Learning: A Case Study of Prospective Teachers in Japan
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.
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DOI: http://dx.doi.org/10.21462/jeltl.v9i2.1319
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