Thiết kế câu hỏi theo hướng tiếp cận PISA trong dạy học phần “Điện” (Khoa học tự nhiên 9) với sự hỗ trợ của trí tuệ nhân tạo
Tóm tắt
In the context of competency-based teaching and the rapid development of artificial intelligence (AI), supporting teachers in designing questions using the PISA approach has become a practical requirement. This paper uses a document analysis and synthesis method to propose a process for designing questions using the PISA approach with the support of AI and illustrates this process through the construction of a question system for the “Electricity” section (Natural Science 9). The process consists of 5 steps, in which AI assists in building context, suggesting questions and answers, while the teacher plays a leading role in selecting, evaluating, and refining the product. Expert feedback on the designed question system shows that the system is rated “High” to “Very High” in terms of necessity, quality, effectiveness of AI use, and practical applicability, with an overall average score of 4.22 and a Cronbach's Alpha coefficient of 0.95. However, to ensure scientific accuracy and pedagogical effectiveness, AI-generated products need to be verified, adjusted, and refined by teachers before being used in practical teaching.
Tài liệu tham khảo
Bộ GD-ĐT (2018). Chương trình giáo dục phổ thông: Chương trình tổng thể (ban hành kèm theo Thông tư số 32/2018/TT-BGDĐT ngày 26/12/2018 của Bộ trưởng Bộ GD-ĐT).
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating? Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228-239. https://doi.org/10.1080/14703297.2023.2190148
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F.,… Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Mislevy, R. J., Steinberg, L. S., & Almond, R. G. (2003). On the structure of educational assessments. Measurement: Interdisciplinary Research and Perspectives, 1(1), 3-67. https://doi.org/10.1207/S15366359MEA0101_02
OECD (2019). PISA 2018 assessment and analytical framework. OECD Publishing. https://doi.org/10.1787/b25efab8-en
OECD (2023). PISA 2022 results: Volume I: The state of learning and equity in education. OECD Publishing. https://doi.org/10.1787/53f23881-en
OECD (2025). PISA 2025 science framework. OECD Publishing.
Pellegrino, J. W., Chudowsky, N., & Glaser, R. (2001). Knowing what students know: The science and design of educational assessment. National Academy Press. https://doi.org/10.17226/10019
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International Journal of Medical Education, 2, 53-55. https://doi.org/10.5116/ijme.4dfb.8dfd
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10, article number 15. https://doi.org/10.1186/s40561-023-00237-x
UNESCO (2021). AI and education: Guidance for policy-makers. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000376709
UNESCO (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693
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