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Cited by 28Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationScience Education and Pedagogy

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Intersections of Mind and Machine: Navigating the Nexus of Artificial Intelligence, Science Education, and the Preparation of Pre-service Teachers

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Cooper, Grant, Tang, Kok-Sing, Fitzgerald, Angela·2025·Journal of Science Education and Technology
DOI 10.1007/s10956-025-10200-9
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Abstract

The rapid advancement of generative artificial intelligence (GenAI) is reshaping various sectors, including education. This editorial explores the intersections of AI, science education, and the preparation of pre-service teachers (PSTs), questioning whether GenAI represents a truly transformative technology or merely the latest iteration of an educational hype cycle. While previous technological innovations—such as radio, television, and computers—were initially heralded as revolutionary, their impact on education has varied significantly. The emergence of GenAI, particularly large language models, introduces new possibilities for personalised learning, digital tutoring, and adaptive content generation, yet its integration into educational practice remains uneven. Despite growing societal reliance on GenAI, a substantial proportion of in-service teachers report limited use, citing a lack of training and institutional policies as key barriers. In contrast, research suggests that PSTs are already engaging with GenAI tools for lesson planning, content summarisation, and assessment preparation. This editorial underscores the need for initial teacher education (ITE) programmes to provide structured support, ensuring that PSTs develop both technical proficiency and critical AI literacy. Ethical concerns—such as data privacy, algorithmic bias, and epistemic authority—must also be central to teacher training. Focusing on science education, this special issue examines the opportunities and challenges of GenAI integration. The contributions explore PSTs’ perceptions, competencies, and preparedness to implement AI-driven tools in their teaching, addressing themes such as inquiry-based learning, pedagogical content knowledge, and the evolving role of educators in AI-powered classrooms. The discussion highlights the necessity of balancing technological innovation with critical reflection, equipping future teachers to navigate the complexities of AI in education responsibly. This editorial aims to advance the dialogue on GenAI’s role in teacher preparation, urging educational institutions to proactively support PSTs in harnessing its potential while fostering a critical, ethical, and pedagogically sound approach to AI integration in science education.

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  • GenAI systems are no longer perceived as occasional technological aids, but rather as normalized components of academic learning ecosystems integrated into writing, information retrieval, summarization, brainstorming, and assessment preparation processes.

    “Recent evidence suggests that these systems are no longer perceived as occasional technological aids, but rather as normalized components of academic learning ecosystems integrated into writing, information retrieval, summarization, brainstorming, and assessment preparation processes (Cooper et al., 2025;Susena et al., 2026).”

    From Between cognitive offloading and critical autonomy: A systematic review of the epistemic implications of generative AI in higher education. (2026) · cites this paper as Cooper et al., 2025

    The Introduction (page not supplied) reports that pre-service teachers use GenAI for lesson ideas, summarisation and assessment preparation, and describes ChatGPT being used for queries. The section on implications for science education also discusses support for writing. But the paper does not establish that GenAI is a normalized component of academic learning ecosystems generally; it says integration remains uneven, particularly among in-service teachers. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Citations

Cites

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Research works printed but not linked: 18

  1. Arantes, J. (2024). Understanding intersections between GenAI and pre-service teacher training: What do we need to understand about the changing face of truth in science education? Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10189-7 · DOI
  2. Blonder, R., Feldman-Maggor, Y., & Rap, S. (2024). Are they ready to teach? Generative AI as a means to uncover pre-service science teachers’ PCK and enhance their preparation program. Journal of Science Education and Technology. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10180-2 · DOI
  3. Chen, B, Zhu, X., & Diaz del Castillo H, F. (2023). Integrating generative AI in knowledge building, Computers and Education: Artificial Intelligence, 5. https://doi.org/10.1016/j.caeai.2023.100184. · DOI · Paper page
  4. Cooper, G., & Tang, K.-S. (2024). Pixels and pedagogy: Examining science education imagery by generative artificial intelligence. Journal of Science Education and Technology, 33(4), 556–568. https://doi.org/10.1007/s10956-024-10104-0 · DOI
  5. Cooper, G., Tang, K.-S., & Rappa, N. (2024). Generative artificial intelligence as epistemic authority? In Artificial Intelligence Applications in Higher Education (1st ed.). Routledge. · DOI · Paper page
  6. Darics, E., & Poppel, L. (2023). Debate: ChatGPT offers unseen opportunities to sharpen students’ critical skills. The Conversation. https://theconversation.com/debate-chatgpt-offers-unseen-opportunities-to-sharpen-students-critical-skills-199264​ · DOI
  7. Duan, S., Exter, M., & Li, Q. (2024). In their ideal future, are preservice teachers willing to integrate technology in their teaching and why? TechTrends, 68(4), 734–748. https://doi.org/10.1007/s11528-024-00978-7 · DOI
  8. Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., Järvelä, S., Mavrikis, M., & Rienties, B. (2024). The promise and challenges of generative AI in education. Behaviour and Information Technology, 1–27. https://doi.org/10.1080/0144929X.2024.2394886 · DOI · Paper page
  9. Huang, H.-W., Teng, D.C.-E., & Tiangco, J. A. N. Z. (2024). The impact of AI chatbot-supported guided discovery learning on pre-service teachers’ learning performance and motivation. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10179-9 · DOI · Paper page
  10. Kent, T. W., & McNergney, R. F. (1999). Will technology really change education? From blackboard to Web. Corwin Press, Inc., A Sage Publications Company, 2455 Teller Road, Thousand Oaks, CA 91320.
  11. Leahy, S. M., Holland, C., & Ward, F. (2019). The digital frontier: Envisioning future technologies impact on the classroom. Futures, 113, 102422. https://doi.org/10.1016/j.futures.2019.04.009 · DOI
  12. Li, Z., & Ironsi, C. S. (2024). The efficacy of generative artificial intelligence in developing science education preservice teachers’ writing skills: An experimental approach. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10148-2 · DOI
  13. Mohamed, S., Png, M.-T., & Isaac, W. (2020). Decolonial AI: Decolonial theory as sociotechnical foresight in artificial intelligence. Philosophy and Technology, 33(4), 659–684. https://doi.org/10.1007/s13347-020-00405-8 · DOI · Paper page
  14. Park, H., Thomson, E., & Cooper, G. (in press). Lesson planning with generative artificial intelligence: Insights from preservice teachers. In Generative AI in Education: Innovations, Challenges, and Future Prospects. Springer.
  15. Ramnarain, U., Ogegbo, A., Penn, M., Ojetunde, S., & Mdlalose, N. (2024). Pre-service science teachers’ intention to use generative artificial intelligence in inquiry-based teaching. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10159-z · DOI
  16. Tang, K. S., & Cooper, G. (2024). The role of materiality in an era of generative artificial intelligence. Science and Education. https://doi.org/10.1007/s11191-024-00508-0 · DOI
  17. Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 11(2), 1–21. · DOI · Paper page
  18. Zhai, X. (2024). Transforming teachers’ roles and agencies in the era of generative AI: Perceptions, acceptance, knowledge, and practices. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10174-0 · DOI · Paper page

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Metadata

Authors
Cooper, G., Tang, K.-S., & Fitzgerald, A.
Affiliations
  • Curtin University (AU)
  • Higher Colleges of Technology (AE)
Corresponding author
Cooper, Grant
ORCID
Venue
Journal of Science Education and Technology
Publisher
Springer Science and Business Media LLC
Published
2025-02-10
Volume
34
Issue
6
Pages
1255-1259
ISSN
  • 1059-0145 (ISSN-L)
  • 1573-1839
DOI
10.1007/s10956-025-10200-9
OpenAlex
W4407344889
Cited by
28
References
1
Citations by year
2026: 21 · 2025: 7
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Science Education and Technology (publishedVersion, cc-by)
  • eSpace (Curtin University) (submittedVersion, cc-by)
Funders
Curtin University
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Education › Science Education and Pedagogy
Keywords
generative AI, pre-service teachers, initial teacher education, science education, AI literacy, large language models, teacher readiness, ethical use of AI, data privacy, algorithmic bias, pedagogical content knowledge, inquiry-based learning, artificial intelligence in education

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