The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Using ChatGPT for Science Learning: A Study on Pre-service Teachers' Lesson Planning
Open accessAbstract
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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Metadata
- Authors
- Lee, G.-G., & Zhai, X.
- Affiliations
- University of Georgia (US)
- Mary Frances Early College of Education, University of Georgia, Athens, GA, USA
- Venue
- IEEE Transactions on Learning Technologies
- Published
- 2024
- Volume
- 17
- Pages
- 1643-1660
- ISSN
- 1939-1382 (ISSN-L)
- 2372-0050
- OpenAlex
- W4396941333
- Semantic Scholar
- 7223eb86633616cecf484006cad2584655fed90b
- Cited by
- 269
- References
- 53
- Citations by year
- 2026: 135 · 2025: 109 · 2024: 25
- FWCI
- 106.03
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Learning Technologies (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (cc-by-nc-nd)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Education and Technology Integration
- Keywords
- ChatGPT, generative AI in education, science learning, pre-service teachers, lesson planning, TPACK, elementary science teaching, teacher perceptions, AI hallucination, teacher-student relationship, self-directed learning
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- Lee, G.-G., & Zhai, X. (2024). Using ChatGPT for Science Learning: A Study on Pre-service Teachers' Lesson Planning. IEEE Transactions on Learning Technologies. https://doi.org/10.1109/tlt.2024.3401457
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