SUN’IY INTELLEKT YORDAMIDA TA’LIM OLUVCHILARNING MANTIQIY FIKRLASHINI MOSLASHUVCHAN RIVOJLANTIRISH MODELI
Keywords:
Ta’limda sun’iy intellekt, pretsedentlarga asoslangan mulohazalash (CBR), dialogli tavsiya tizimlari, ta’lim trayektoriyalari, ta’lim ma’lumotlarini tahlil qilish, tushuntiriladigan sun’iy intellekt, HEMIS.Abstract
Raqamli texnologiyalar va sun’iy intellektning tez sur’atlarda rivojlanishi sharoitida oliy ta’limda shaxsga yo‘naltirilgan karyera maslahatlari berishga bo‘lgan ehtiyoj tobora ortib bormoqda. Psixometrik testlar yoki statik tavsiya modellari kabi an’anaviy usullar ko‘pincha uzoq muddatli ta’lim yo‘nalishlarini hisobga olmaydi va tushuntirilishi qiyin bo‘lgan tavsiyalar beradi. Maqolada pretsedentlarga asoslangan mulohazalar (CBR) va uning muloqotli kengaytmasi (CCBR) yondashuvi taklif etilgan bo‘lib, u talabalarning ta’lim tarixini tuzilmali holatlar sifatida modellashtiradi va to‘liq bo‘lmagan ma’lumotlarni interaktiv tarzda aniqlashtirish imkonini beradi.
References
1. Herath, G. A. C. A., Kumara, B. T. G. S., Rathnayaka, R. M. K. T., & Ishanka, U. A. P. (2023). Computer-assisted career guidance tools for students’ career path planning: A review on enabling technologies and applications. Journal of Information Technology Education Research, 23 (2024), 006 https://doi.org/10.28945/5265
2. Trujillo, F., Pozo, M., & Suntaxi, G. (2025). Artificial intelligence in education: A systematic literature review of machine learning approaches in student career prediction. Journal of Technology and Science Education, 15(1), 162-185. https://doi.org/10.3926/jotse.3124
3. Manganello, F. (2023). Machine learning for post-diploma educational and career guidance: A scoping review in AI-driven decision support systems. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2025.1578979
4. Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.
5. Kolodner, J. L. (1993). Case-based reasoning. Morgan Kaufmann.
6. Watson, I. (1997). Applying case-based reasoning: Techniques for enterprise systems. Morgan Kaufmann.
7. López de Mántaras, R., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M. L., Cox, M. T., Forbus, K., Keane, M., & Aamodt, A. (2005). Retrieval, reuse, revision and retention in case-based reasoning. The Knowledge Engineering Review, 20(3), 215–240. https://doi.org/10.1017/S0269888906000646.
8. Aha, D. W., Breslow, L. A., & Muñoz-Avila, H. (2001). Conversational case-based reasoning. Applied Intelligence, 14(1), 9–32. https://doi.org/10.1023/A:1008346807097.
9. Hillig, S., & Müller, R. (2019). How do conversational case-based reasoning systems interact with their users? A literature review. User Modeling and User-Adapted Interaction, 29, 1–42. https://doi.org/10.1080/0144929X.2020.1767207
10. Zarandi, M. H. F., Zolghadri, M., & Turksen, I. B. (2011). A fuzzy case-based reasoning approach to value engineering. Expert Systems with Applications, 38(10), 12127–12135. https://doi.org/10.1016/j.eswa.2011.03.046.
11. Khan, M. J. (2014). Performance evaluation of fuzzy clustered case-based reasoning. Expert Systems with Applications, 41(4), 1656–1666. https://doi.org/10.1080/0952813X.2020.1744194
12. Lorenzi, F., & Ricci, F. (2005). Case-based recommender systems: A unifying view. In Intelligent techniques for web personalization (Lecture Notes in Computer Science, Vol. 3169, pp. 89–113). Springer. https://doi.org/10.1007/978‑3‑540‑28540‑0_5
13. Lahoud, C., Ayoub, J., & Khoury, R. (2023). A comparative analysis of recommender systems for university major and career domain guidance. Education and Information Technologies, 28, 1–28. https://doi.org/10.1007/s10639-022-11541-3.
14. Zhang, X. (2025). Logistics project risk response decision-making for global supply chain resilience and agility: An optimized case-based reasoning approach. Journal of Intelligent & Fuzzy Systems. https://doi.org/10.1080/00207543.2024.2414374.
