Thiết kế mô hình AI tác nhân hỗ trợ đánh giá, phản hồi trong dạy học Toán ở trường phổ thông
Tóm tắt
Modern mathematics education requires the timely diagnosis of students’ systematic errors and misconceptions. This study designs and evaluates the effectiveness of a Multi-Agent System operating within a human-in-the-loop framework, focusing on three core functions: (1) in-depth analysis of error data; (2) automated generation of analogous learning content to reinforce conceptual understanding; and (3) provision of adaptive feedback tailored to individual learning needs. The research employs a Design-Based Research (DBR) approach to develop an integrated framework that combines principles of mathematics didactics with Generative AI technologies. The findings contribute a feasible theoretical framework and a practical tool to support teachers’ timely instructional interventions, thereby enhancing personalized learning outcomes and contributing to the digital transformation of mathematics education.
Tài liệu tham khảo
Garzón, J., Patiño, E., & Marulanda, C. (2025). Systematic review of artificial intelligence in education: Trends, benefits, and challenges. Multimodal Technologies and Interaction, 9(8), 84. https://doi.org/10.3390/mti9080084
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81-112. https://doi.org/10.3102/003465430298487
Huang, Y. (2024). Levels of AI agents: From rules to large language models [Preprint]. arXiv. https://arxiv.org/abs/2405.06643
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
Li, H. (2026). General framework of AI agents. Journal of Computer Science and Technology. https://doi.org/10.1007/s11390-025-5951-5
Maksimchuk, M., Roeber, E., & Store, D. (2025). Generative AI in the K-12 Formative Assessment Process: Enhancing Feedback in the Classroom. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers (pp. 107-110).
Trần Trung, Đỗ Mạnh Hùng, Nguyễn Thị Kim Sơn (2025). Hệ thống AI tác nhân trong phân tích nội dung tự động của tập dữ liệu giáo dục. Tạp chí Giáo dục, 25(11), 1-4. https://tcgd.tapchigiaoduc.edu.vn/index.php/
tapchi/article/view/3440
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., ... & Gui, T. (2023). The rise and potential of large language model based agents: A survey. arXiv preprint arXiv:2309.07864.
Yingzhe, L. I. (2025). Addressing “hallucinations” in AI-generated content: Strategies for developing student fact-checking and information evaluation skills. Artificial Intelligence Education Studies, 1(2), 48-62. https://doi.org/
6914/aiese.010204
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., & Spector, J. M. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021.
Tải xuống
Đã Xuất bản
Cách trích dẫn
Số
Chuyên mục
Giấy phép
Tác phẩm này được cấp phép theo Ghi nhận tác giả của Creative Commons Giấy phép quốc tế 4.0 .
