Using ChatGPT for Science Learning: A Study on Pre-service Teachers' Lesson Planning

Abstract

While ongoing efforts have continuously emphasized the integration of ChatGPT with science teaching and learning, there are limited empirical studies exploring its actual utility in the classroom. This study aims to fill this gap by analyzing the lesson plans developed by 29 pre-service elementary teachers and assessing how they integrated ChatGPT into science learning activities. We first examined how ChatGPT was integrated with the subject domains, teaching methods/strategies and then evaluated the lesson plans using a GenAI-TPACK-based rubric. We further examined pre-service teachers' perceptions and concerns about integrating ChatGPT into science learning. Results show a diverse number of ChatGPT applications in different science domains—e.g., Biology (9/29), Chemistry (7/29), and Earth Science (7/29). Fourteen types of teaching methods/strategies were identified in the lesson plans. On average, the pre-service teachers' lesson plans scored high on the modified TPACK-based rubric (M = 3.29; SD = .91; on a 1-4 scale), indicating a reasonable envisage of integrating ChatGPT into science learning, particularly in ‘instructional strategies & ChatGPT’ (M = 3.48; SD = .99). However, they scored relatively lower on exploiting ChatGPT's functions toward its full potential (M = 3.00; SD = .93), compared to other aspects. We also identified several inappropriate use cases of ChatGPT in lesson planning (e.g., as a source of hallucinated internet material and technically unsupported visual guidance). Pre-service teachers anticipated ChatGPT to afford high-quality questioning, self-directed learning, individualized learning support, and formative assessment. Meanwhile, they also expressed concerns about its accuracy and the risks that students may be overly dependent on ChatGPT. They further suggested solutions to systemizing classroom dynamics between teachers and students. The study underscores the need for more research on the roles of generative AI in actual classroom settings and provides insights for future AI-integrated science learning.

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