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<p>In the context of the rapid development of generative artificial intelligence (GAI), how to effectively enhance pre-service teachers’ AI literacy and critical thinking has become an important issue in the field of educational technology. This study adopted a quasi-experimental design, with 92 pre-service teachers as the research subjects, dividing them into an experimental group receiving GAI digital storytelling instruction assisted by the CLEAR framework and a control group receiving conventional GAI digital storytelling instruction. The effects of different teaching methods were evaluated using Mann-Whitney U tests and covariance analysis. The findings revealed that, compared to conventional methods, the CLEAR framework significantly improved the quality of pre-service teachers’ digital storytelling and enhanced their AI literacy and critical thinking. Furthermore, qualitative analysis of interview data provided corroborating evidence for these results. The study’s outcomes validate the effectiveness of the CLEAR framework in promoting reflective interaction and deep learning, expand the application of constructivist theory in AI-assisted teaching contexts, and provide a new structured teaching strategy for digital literacy education practices.</p>

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Fostering AI literacy and critical thinking in pre-service teachers through GAI-based digital storytelling within the CLEAR framework

  • 期刊论文

  • 发表日期：

    2025年9月22日

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  • Chuang-Qi Chen

摘要

In the context of the rapid development of generative artificial intelligence (GAI), how to effectively enhance pre-service teachers’ AI literacy and critical thinking has become an important issue in the field of educational technology. This study adopted a quasi-experimental design, with 92 pre-service teachers as the research subjects, dividing them into an experimental group receiving GAI digital storytelling instruction assisted by the CLEAR framework and a control group receiving conventional GAI digital storytelling instruction. The effects of different teaching methods were evaluated using Mann-Whitney U tests and covariance analysis. The findings revealed that, compared to conventional methods, the CLEAR framework significantly improved the quality of pre-service teachers’ digital storytelling and enhanced their AI literacy and critical thinking. Furthermore, qualitative analysis of interview data provided corroborating evidence for these results. The study’s outcomes validate the effectiveness of the CLEAR framework in promoting reflective interaction and deep learning, expand the application of constructivist theory in AI-assisted teaching contexts, and provide a new structured teaching strategy for digital literacy education practices.