The article is dedicated to the development of an informational analytical-intellectual system for assessing the quality of educational content produced by the graduating department, using information-extreme intellectual technology. The research focuses on improving the accuracy of classifying educational materials according to the evaluations of graduates and employers, considering the dynamics of the labor market. The main attention is on developing a mathematical model of the machine learning process for a system that classifies the quality of educational materials based on the assessments of employers and graduates. A mathematical model for the operation of the analytical intellectual analysis system has been developed, based on the concept of nested hyperspherical containers of quality classes. Algorithms for parallel and sequential optimization of assessment feature tolerance limits have been developed, allowing the improvement of the system's functional efficiency under conditions of limited input data. The optimal geometric parameters of containers for each quality class have been defined in accordance with the Kullback information criterion. Experimental results show a significant improvement in classification accuracy - from 55% to 97%. The obtained results confirm the feasibility of using IEI technology for assessing the quality of educational materials in higher education. The proposed approach allows adapting the educational content to the current market requirements, which contributes to improving the competitiveness of graduates. A promising direction for further research is the integration of hybrid intelligent methods, including neuro-fuzzy models, as well as expanding the system's functional capabilities in the context of automated monitoring of the quality of the educational process.

intellectual data analysis, assessment of functional efficiency, information-extreme intellectual technology, analytical-informational system, classifier

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1. Meylani, R. (2024). A comparative analysis of traditional and modern approaches to assessment and evaluation in education. Journal of Educational Research, 15(1), 520–555. https://doi.org/10.51460/baebd.1386737.

2. Kintanar, F., Elladora, S. T., Genabe, V. C., & Rendon, Z. L. L. (2021). Perceived limitations of e-learning platforms. International Journal of Educational Research, 11(2), 157–172.

3. Dovbysh, A. S. (2009). Osnovy proiektuvannia intelektualnykh system [Fundamentals of intelligent systems design]. Sumy: SumDU. [in Ukrainian].

4. Prylepa, D. V. (2024). Informatsiino-ekstremalna intelektualna tekhnolohiia diahnostuvannia emotsiino- psykhichnoho stanu liudyny [Information-extreme intellectual technology for diagnosing the emotional and mental state of a person] (Doctoral dissertation). Kharkiv [in Ukrainian].

5. Ezzahraa Elhabti, F. (2025). Enhancing student performance prediction in e-learning ecosystems using machine learning techniques. International Journal of Information and Education Technology, 15(2), 301– 311. https://doi.org/10.18178/ijiet.2025.15.2.2243.

6. Sayed, B. T., Madanan, M., & Biju, N. (2022). Intelligent systems to measure the quality of assessments in higher education. Journal of Management Information and Decision Sciences, 25(S6), 1–11.

7. Dovbysh, A. S., Shelehov, I. V., Khibovska, Y. O., & Matiash, O. V. (2021). Informatsiino-analitychna systema otsiniuvannia vidpovidnosti suchasnym vymoham navchalnoho kontentu spetsialnosti kiberbezpeka [Information-analytical system for assessing compliance of cybersecurity educational content with modern requirements]. Radioelektronika i kompiuterni systemy, 1(197), 70. https://doi.org/10.32620/reks.2021.1.06 [in Ukrainian].

8. Zhang, Y., & Li, X. (2025). Student achievement prediction and auxiliary improvement method based on fuzzy decision support system. Discover Artificial Intelligence, 5(63), 1–14. https://doi.org/10.1007/s44163-025-00308-7.

9. Senkivskyy, V., Sikora, L., Lysa, N., Kudriashova, A., & Pikh, I. (2025). Fuzzy system for the quality assessment of educational multimedia edition design. Applied Sciences, 15(8), 4415. https://doi.org/10.3390/app15084415.

10. Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2024). Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. Information, 15(10), 596. https://doi.org/10.3390/info15100596.

11. Tavakoli, M., Mol, S. T., & Kismihók, G. (2020). Labour market information driven, personalized, OER recommendation system for lifelong learners. arXiv preprint, 96–104. https://doi.org/10.48550/arXiv.2005.07465.

12. Hladchenko, M. S. (2019). Informatsiina systema intelektualnoho analizu yakosti osvitnoho kontentu kafedry [Information system for intelligent analysis of the quality of educational content of the department] (Master’s thesis, SumDU). Sumy: SumDU. http://essuir.sumdu.edu.ua/handle/123456789/76815 (Accessed: 06.06.2025). [in Ukrainian].

13. Shelehov, I. V., Prylepa, D. V., Khibovska, Y. O., & Otroshcenko, M. S. (2023). Machine learning decision support systems for adaptation of educational content to the labor market requirements. Radio Electronics, Computer Science, Control, 1(62), 62–72. https://doi.org/10.15588/1607-3274-2023-1-6.

14. Shelehov, I. V., Prylepa, D. V., Zborshchyk, O. P., & Mishchenko, A. Ye. (2022). Pobudova vyrishalnykh pravyl dlia SPPR adaptatsii navchalnoho kontentu vypusknoi kafedry do vymoh rynku pratsi [Construction of decision rules for DSS adaptation of graduation department’s educational content to labor market requirements]. Patent RF No. 6551. [in Ukrainian].

15. Moskalenko, V. V., Moskalenko, A. S., & Korobov, A. G. (2018). Models and methods of intellectual information technology of autonomous navigation for compact drones. Radio Electronics, Computer Science, Control, 3, 68–77. https://doi.org/10.15588/1607-3274-2018-3-8.

16. Dorenskyi, O., Drobko, O., & Drieiev, O. (2022). Improved model and software of the digital information service of the municipal health care institutions. Central Ukrainian Scientific Bulletin. Technical Sciences, 5(36, Part II), 3–10. https://doi.org/10.32515/2664-262X.2022.5(36).2.3-10.

17. Korniienko, O., Kozub, N., & Dorenskyi, O. (2025). Method and technological solution of an AI-based adaptive investor survey service for determining an individual risk profile. Central Ukrainian Scientific Bulletin. Technical Sciences, 11(42, Part II), 3–10. doi.org/10.32515/2664-262X.2025.11(42).1.3-10.

18. Kachurivskyi, V., Kotovskyi, A., Lykhodid, T., Kachurivska, H., & Dorenskyi, O. (2025). The concept of digital transformation of monitoring scientific activity of participants in educational process of the Ukrainian HEI. Central Ukrainian Scientific Bulletin. Technical Sciences, 11(42, Part I), 27–36. https://doi.org/10.32515/2664-262X.2025.11(42).1.27-36.