REIMAGINING PRIMARY PHYSICS EDUCATION THROUGH ARTIFICIAL INTELLIGENCE: PEDAGOGICAL APPLICATIONS OF CHATGPT

Konstantinos T. Kotsis

Abstract


This study examines the pedagogical potential of Artificial Intelligence (AI), particularly big language models like ChatGPT, in elementary science education, emphasizing physics instruction.  The article, which is grounded in modern constructivist and inquiry-based learning theories, explores how AI tools might assist young learners in cultivating scientific comprehension through dialogic engagement, scaffolded inquiry, and contextually enriched explanations.  The analysis synthesizes previous worldwide research, demonstrating ChatGPT's ability to generate narrative-driven learning experiences, develop age-appropriate experimental activities, and rectify prevalent errors in fundamental physics concepts, including force, motion, and energy. From an educational standpoint, AI serves as both a teaching resource and a cognitive instrument, facilitating varied learning and advancing student-centered methodologies.  ChatGPT's versatility enables educators to incorporate it into many educational settings, providing tailored explanations, exemplifying scientific reasoning, and fostering inquiry-based debates.  The study highlights its significance in assisting non-specialist educators by offering immediate help in lesson preparation, experimental design, and formative evaluation, thus improving teacher confidence and instructional quality. Effective integration relies on providing educators with AI pedagogical literacy—competencies to critically assess AI outputs, connect them with curriculum objectives, and cultivate epistemically rich learning environments.  The article warns against excessive dependence on AI, emphasizing the necessity of maintaining the primacy of teacher mediation and inquiry-based pedagogy. This paper presents AI to enhance, rather than supplant, human instruction, positioning ChatGPT as a driver of pedagogical innovation in primary scientific education.  It asserts that strategically deployed AI can enhance early science education by promoting curiosity, profound conceptual comprehension, and active engagement in scientific inquiry processes.

 

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artificial intelligence; pedagogical innovation; primary science education; inquiry-based learning; physics teaching

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References


Appleton, K. (2008). Developing science pedagogical content knowledge through mentoring elementary teachers. Journal of Science Teacher Education, 19(6), 523–545. https://doi.org/10.1007/s10972-008-9109-4

Binns, R., Veale, M., Van Kleek, M., & Shadbolt, N. (2018). 'It's reducing a human being to a percentage': Perceptions of justice in algorithmic decisions. CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3173574.3173951

Black, P., & Wiliam, D. (2009). Developing the theory of formative assessment. Educational Assessment, Evaluation and Accountability, 21(1), 5–31. https://doi.org/10.1007/s11092-008-9068-5

Bruner, J. (1996). The Culture of Education. Harvard University Press.

diSessa, A. A. (2018). A history of conceptual change research: Threads and fault lines. In S. Vosniadou (Ed.), International Handbook of Research on Conceptual Change (2nd ed., pp. 1–25). Routledge. https://doi.org/10.4324/9780203154472

Egan, K. (1988). Teaching as Storytelling: An Alternative Approach to Teaching and Curriculum in the Elementary School. University of Chicago Press.

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5

Harlen, W. (2014). Working with Big Ideas of Science Education. Global Network of Science Academies (IAP) Science Education Programme. https://doi.org/10.13140/RG.2.1.1999.4081

Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign. https://doi.org/10.1038/s41539-022-00127-1

Kind, V. (2014). A degree is not enough: A quantitative study of aspects of pre-service secondary science teachers’ knowledge of physics. International Journal of Science Education, 36(8), 1313–1345. https://doi.org/10.1080/09500693.2013.860497

Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236

Kotsis, K. (2024a). Artificial Intelligence Creates Fairy Tales for Physics Teaching in Primary Education. European Journal of Open Education and E-learning Studies, 9(1), 1–16. http://dx.doi.org/10.46827/ejoe.v9i1.5250

Kotsis, K. (2024b). ChatGPT Develops Physics Experiment Worksheets for Primary Education Teachers. European Journal of Education Studies, 11(5), 1–20. http://dx.doi.org/10.46827/ejes.v11i5.5274

Kotsis, K. (2024c). ChatGPT in Teaching Physics Hands-On Experiments in Primary School. European Journal of Education Studies, 11(10), 126–143. https://doi.org/10.46827/ejes.v11i10.5549

Kotsis, K. T. (2024d). The Scientific Literacy Enables Policymakers to Legislate on Artificial Intelligence. European Journal of Political Science Studies, 7(1), 69–83. https://doi.org/10.46827/ejpss.v7i1.1682

Kotsis, K. T. (2024e). Artificial Intelligence Creates Plagiarism or Academic Research?. European Journal of Arts, Humanities and Social Sciences, 1(6), 169–179. https://doi.org/10.59324/ejahss.2024.1(6).18

Kotsis, K. T. (2024f). Artificial Intelligence for Writing Academic Papers in Education. Journal of Contemporary Philosophical and Anthropological Studies, 3(1), 10–19. https://doi.org/10.59652/jcpas.v3i1.375

Kotsis, K. T. (2024g). Artificial Intelligence Helps Primary School Teachers to Plan and Execute Physics Classroom Experiments. EIKI Journal of Effective Teaching Methods, 2(2), 1–9. https://doi.org/10.59652/jetm.v2i2.158

Kotsis, K. T. (2024i). Correcting Students’ Misconceptions in Physics Using Experiments Designed by ChatGPT. European Journal of Contemporary Education and E-Learning, 2(2), 83–100. https://doi.org/10.59324/ejceel.2024.2(2).07

Kotsis, K. T. (2024j). ChatGPT into the Inquiry-Based Science Curriculum for Primary Education. European Journal of Education and Pedagogy, 5(6), 28–34. https://doi.org/10.24018/ejedu.2024.5.6.891

Kotsis, K. T. (2024k). Integration of Artificial Intelligence in Science Teaching in Primary Education: Applications for Teachers. European Journal of Contemporary Education and E-Learning, 2(3), 27–43. https://doi.org/10.59324/ejceel.2024.2(3).04

Kotsis, K. T. (2025a). Artificial Intelligence and the Scientific Process: A Review of ChatGPT’s Role to Foster Experimental Thinking in Physics Education. European Journal of Contemporary Education and E-Learning, 3(3), 183–198. https://doi.org/10.59324/ejceel.2025.3(3).14

Kotsis, K. T. (2025b). ChatGPT and DeepSeek Evaluate One Another for Science Education. EIKI Journal of Effective Teaching Methods, 3(1), 98–102. https://doi.org/10.59652/jetm.v3i1.439

Kotsis, K. T. (2025c). Comparing ChatGPT and DeepSeek in Addressing Misconceptions about Physics Concepts. European Journal of Contemporary Education and E-Learning, 3(2), 191–206. https://doi.org/10.59324/ejceel.2025.3(2).17

Kotsis, K. T. (2025d). From Chalkboard to Chatbot: The Future of Physics Education through Artificial Intelligence Integration. EIKI Journal of Effective Teaching Methods, 3(2), 74–79. https://doi.org/10.59652/jetm.v3i2.515

Kotsis, K. T. (2025e). Integrating Artificial Intelligence for Science Teaching in High School. LatIA, 3, 89. https://doi.org/10.62486/latia202589

Kotsis, K. T. (2025f). Issues between Artificial Intelligence and Personal Data in Education. International Research in Education, 13(1), 45–65. https://doi.org/10.5296/ire.v13i1.22850

Kotsis, K. T. (2025g). Misconceptions about Artificial Intelligence from Preservice Teachers: A Literature Review. EIKI Journal of Effective Teaching Methods, 3(2). https://doi.org/10.59652/jetm.v3i2.565

Kotsis, K. T. (2025h). Transforming Misconceptions into Knowledge: The Use of Artificial Intelligence in Teaching Electromagnetic Radiation. European Journal of Open Education and E-learning Studies, 10(3), 1–16. https://doi.org/10.46827/ejoe.v10i3.6081

Kotsis, K. T., & Tsiouri, E. (2024). Utilizing ChatGPT for Primary School Earthquake Education. European Journal of Contemporary Education and E-Learning, 2(4), 145–157. https://doi.org/10.59324/ejceel.2024.2(4).12

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson Education. https://doi.org/10.13140/RG.2.1.2281.1601

Mercer, N., Hennessy, S., & Warwick, P. (2017). Dialogue, thinking together and digital technology in the classroom: Some educational implications of a continuing line of inquiry. International Journal of Educational Research, 97, 187–199. https://doi.org/10.1016/j.ijer.2017.08.007

Osborne, J., Simon, S., Christodoulou, A., Howell-Richardson, C., & Richardson, K. (2016). Learning to argue: A study of four schools and their attempt to develop the use of argumentation as a common instructional practice and its impact on students. Journal of Research in Science Teaching, 53(6), 893–921. https://doi.org/10.1002/tea.21316

Roll, I., & Wylie, R. (2016). Evolution and Revolution in Artificial Intelligence in Education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3

Samara, V., & Kotsis, K. T. (2024). Use of the Artificial Intelligence in Teaching the Concept of Magnetism in Preschool Education. Journal of Digital Educational Technology, 4(2), ep2419. https://doi.org/10.30935/jdet/14864

UNESCO. (2020). Education for Sustainable Development: A Roadmap. https://unesdoc.unesco.org/ark:/48223/pf0000374802

Vosniadou, S. (2013). International Handbook of Research on Conceptual Change (2nd ed.). Routledge. https://doi.org/10.4324/9780203154472

Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Harvard University Press.

Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995

Woolf, B. P. (2009). Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing E-learning. Morgan Kaufmann. https://doi.org/10.1016/C2009-0-20922-1

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0




DOI: http://dx.doi.org/10.46827/ejes.v12i10.6224

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