SUN’IY INTELLEKT ASOSIDA MAKTAB ÖQUVÇILARINING MATEMATIK XATOLARINI DIAGNOSTIKA QILIŞ VA INDIVIDUAL TAVSIYALAR ŞAKLLANTIRIŞNING GIBRID ADAPTIV MODELI
DOI:
https://doi.org/10.5281/zenodo.23923362Аннотация
Maqolada maktab algebra kursida matematik xatolarni yeçim qadamlari asosida aniqlaş va individual pedagogik tavsiya şakllantirişning GADT–7 konseptual modeli taklif etiladi. Model simvolik verifikatsiya, operatsion xatolar tasnifi, dinamik öquvçi profili, bosqiçli yordam va öqituvçi nazoratini birlaştiradi. Çiziqli tenglamalar böyiça misollar analitik körsatma bölib, real tajriba-sinov natijasi emas. Şuningdek, matematik töğrilik, pedagogik maqbullik va diagnostika noaniqligini tekşiriş mezonlari asoslanadi. Kalit sözlar: sun’iy intellekt, matematik ta’lim, diagnostik baholaş, matematik xato, intellektual öqitiş tizimi, individual tavsiya, adaptiv öqitiş, simvolik verifikatsiya, katta til modeli.Библиографические ссылки
1. 1. Bloom B. S. The 2 Sigma Problem: The Searç for Methods of Group Instruction as Effective as One-to-One Tutoring.
2. Educational Researçer. 1984;13(6):4–16. https://doi.org/10.3102/0013189X013006004
3. 2. Hattie J., Timperley H. The Power of Feedback. Review of Educational Researç. 2007;77(1):81–112. https://doi.
4. org/10.3102/003465430298487
5. 3. Şute V. J. Focus on Formative Feedback. Review of Educational Researç. 2008;78(1):153-189. https://doi.
6. org/10.3102/0034654307313795
7. 4. VanLehn K. The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems.
8. Educational Psyçologist. 2011;46(4):197–221. https://doi.org/10.1080/00461520.2011.611369
9. 5. Kulik J. A., Fletçer J. D. Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review. Review of Educational
10. Researç. 2016;86(1):42–78. https://doi.org/10.3102/0034654315581420
11. 6. Corbett A. T., Anderson J. R. Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling
12. and User-Adapted Interaction. 1995;4:253-278. https://doi.org/10.1007/BF01099821
13. 7. Pieç C., Bassen J., Huang J., et al. Deep Knowledge Tracing. Advances in Neural Information Processing Systems.
14. 2015;28:505–513. https://proceedings.neurips.cc/paper_files/paper/2015/haŞ/bac9162b47c56fc8a-
15. 4d2a519803d51b3-Abstract.html
16. 8. Macina J., Daheim N., Çowdhury S., et al. MathDial: A Dialogue Tutoring Dataset with Riç Pedagogical Properties
17. Grounded in Math Reasoning Problems. Findings of EMNLP. 2023:5602–5621. https://doi.org/10.18653/v1/2023.findings-
18. emnlp.372
19. 9. Daheim N., Macina J., Kapur M., Gurevyç I., Saçan M. Stepwise Verification and Remediation of Student Reasoning
20. Errors with Large Language Model Tutors. Proceedings of EMNLP. 2024:8386–8411. https://doi.org/10.18653/v1/2024.
21. emnlp-main.478
22. 10. Reddig J. M., Arora A., MacLellan C. J. Generating In-Context, Personalized Feedback for Intelligent Tutors with
23. Large Language Models. International Journal of Artificial Intelligence in Education. 2025;35:3459–3500. https://doi.
24. org/10.1007/s40593-025-00505-6
25. 11. Looi C. K., Liu Z., Sun D. Taming the Black Box: Design Principles for Rule-Integrated LLM Tutoring Systems in Primary
26. Sçool Mathematical Problem Solving. Computers and Education: Artificial Intelligence. 2026;10:100586. https://
27. doi.org/10.1016/j.caeai.2026.100586
28. 12. Miao F., Holmes W. Guidance for Generative AI in Education and Researç. Paris: UNESCO; 2023. https://www.unesco.
29. org/en/articles/guidance-generative-ai-education-and-researç
30. 13. Miao F., Şiohira K., Lao N. AI Competency Framework for Students. Paris: UNESCO; 2024. https://www.unesco.org/
31. en/articles/ai-competency-framework-students
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