DESIGNING PHYSICS SIMULATION EXPERIMENTS USING GENERATIVE ARTIFICIAL INTELLIGENCE TO SUPPORT GRADE 7 NATURAL SCIENCE TEACHING
Main Article Content
Abstract
Conducting hands-on experiments in Grade 7 Natural Science teaching is often constrained by limited equipment and classroom conditions. This study proposes a process for designing physics simulation experiments using Generative AI, in which ChatGPT supports scenario development and Canva is used to create interactive simulations. The study was conducted as an application-oriented research combined with an impact survey involving 157 lower secondary school students to evaluate their responses to AI-generated simulation experiments. Results indicate that the simulations effectively visualize physics phenomena, enhance students’ learning interest, increase classroom participation, and support knowledge acquisition. The findings confirm the feasibility of applying Generative AI to digital learning resource development and to the innovation of Natural Science teaching at the lower secondary level.
Keywords
Generative AI, ChatGPT, Canva, physics simulation experiments, Grade 7 Natural Science
Article Details
References
[2] Bộ Giáo dục và Đào tạo (2025), Thông tư số 02/2025/TT-BGDĐT ngày 24 tháng 01 năm 2025 ban hành Khung năng lực số cho người học.
[3] Nguyễn Văn Biên (Chủ biên) (2020), Dạy học phát triển năng lực môn Khoa học tự nhiên ở trường phổ thông, Nxb. Đại học Sư phạm Hà Nội, Hà Nội.
[4] Bitzenbauer, P. (2023), ChatGPT in physics education: A pilot study on easy-to-implement activities, Contemporary Educational Technology, 15(3):429, https://doi.org/10.30935/cedtech/13360
[5] Bybee, R. W. (2020), STEM education and the 21st century workforce. NSTA Press.
[6] Canva. (n.d.), Canva for Education: Empowering classroom creativity, Sydney, Australia, https://www.canva.com/education/
[7] Finkelstein, N. D., Adams, W. K., Keller, C. J., Kohl, P. B., Perkins, K. K., Podolefsky, N. S., & Reid, S. (2005), When learning about the real world is better done virtually: A study of substituting computer simulations for laboratory equipment. Physical Review Special Topics-Physics Education Research, 1(1), Article 010103, https://doi.org/10.1103/PhysRevSTPER.1.010103
[8] Holmes, W., Bialik, M., & Fadel, C. (2022), Artificial intelligence in education: Promises and implications for teaching and learning, Center for Curriculum Redesign, Boston, MA, USA.
[9] Liang, Y., Wang, X., Yang, J., & Liu, H. (2023), Exploring the potential of ChatGPT in physics education, Smart Learning Environments, 10(1):35-49, Springer Nature, London, UK, https://doi.org/10.1186/s40561-023-00242-0
[10] Luckin, R. (2022), AI for school teachers, Routledge, London, UK.
[11] Mayer, R. E. (2021), Multimedia learning (3rd ed.), Cambridge University Press.
[12] OECD. (2021), Digital education outlook 2021: Pushing the frontiers with AI, blockchain and robots, OECD Publishing, Paris, France, https://doi.org/10.1787/589b283f-en
[13] OpenAI. (2023), GPT-4 technical report, San Francisco, CA, USA, https://openai.com/research/gpt-4
[14] Perkins, K., Adams, W., Dubson, M., Finkelstein, N., Reid, S., & Wieman, C. (2006). PhET: Interactive simulations for teaching and learning physics, The Physics Teacher, 44(1):18-23, https://doi.org/10.1119/1.2150754
[15] Đỗ Hương Trà (Chủ biên) (2015), Dạy học tích hợp phát triển năng lực học sinh, Nxb. Đại học Sư phạm Hà Nội, Hà Nội.
[16] UNESCO (2023), Guidance for generative AI in education and research, Paris, France, https://unesdoc.unesco.org/ark:/48223/pf0000386693