kaz44Вестник Университета Шакарима. Серия технические наукиBulletin of Shakarim University. Technical Sciences2788-79953006-0524«Шәкәрім университеті» КеАҚ10.53360/2788-7995-2025-4(20)-8kaz44-2199Research ArticleАВТОМАТИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ (ОРИГИНАЛЬНАЯ СТАТЬЯ)AUTOMATION AND INFORMATION TECHNOLOGY (ORIGINAL ARTICLE)ПРИМЕНЕНИЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ДЛЯ АДАПТИВНОГО ПРОГНОЗИРОВАНИЯ УЧЕБНЫХ ТРАЕКТОРИЙ СТУДЕНТОВUSING ARTIFICIAL INTELLIGENCE TO ADAPT STUDENTS' LEARNING TRAJECTORIEShttps://orcid.org/0000-0001-8237-8549КадеркееваЗ. К.KaderkeyevaZ. K.

Зульфия Кенесовна Кадеркеева – старший преподаватель кафедры технологии искусственного интеллекта 

10000 Қазақстан, Астана қ., Сәтбаев к-сі, 2

Zulfiya Kenesovna Kaderkeyeva – Senior lecturer of the Department of artificial intelligence technologies

10000 Kazakhstan, Astana city, 2 Satbaev st.

kaderkeyeva_zk@enu.kzhttps://orcid.org/0000-0002-9272-8829ОмарбековаА. С.OmarbekovaA. S.

Асель Омарбекова – доцент кафедры технологии искусственного интеллекта

10000 Қазақстан, Астана қ., Сәтбаев к-сі, 2 

Assel Omarbekova – Associate Professor of the Department of artificial intelligence technologies

10000 Kazakhstan, Astana city, 2 Satbaev st.

omarbekova_as@enu.kzhttps://orcid.org/0000-0002-5898-815XМилошМ.MiloszM.

Марек Милош – PhD. профессор 

Польша, Люблинское воеводство, ул. Марии Кюре-Склодовской, 5 

Marek Milosz – received the PhD. (Eng.) degree, University Professor 

Poland, Lublin Voivodeship, 5 Maria Czure-Sklodowska Street

m.milosz@pollub.plhttps://orcid.org/0000-0002-7440-5591БигалиеваЖ. С.BigaliyevaZh. S.

Жанар Серикхановна Бигалиева – старший преподаватель кафедры «Робототехники и технических средств автоматики»

Казахстан, Алматы, ул. Каныша Сатпаева, 22

Zhanar Bigaliyeva – Senior Lecturer of the Department of Robotics and Engineering Tools of Automation

Kazakhstan, Almaty, Kanysh Satpayev Street, 22

zh.bigaliyeva@satbayev.universityhttps://orcid.org/0009-0007-1717-5041БайтургановаВ. К.BaiturganovaV. K.

Винера Канапияевна Байтурганова – старший преподаватель кафедры «Робототехники и технических средств автоматики»

Казахстан, Алматы, ул. Каныша Сатпаева, 22

Vinera Baiturganova – Senior Lecturer of the Department of Robotics and Engineering Tools of Automation

Kazakhstan, Almaty, Kanysh Satpayev Street, 22

v.baiturganova@satbayev.universityЕвразийский национальный университет имени Л.Н. ГумилёваКазахстанL.N. Gumilyov Eurasian National UniversityKazakhstanЛюблинский технологический университетПольшаLublin University of TechnologyPolandСатпаев университетКазахстанSatbayev UniversityKazakhstan20252501202614(20)6572Copyright © Кадеркеева З.К., Омарбекова А.С., Милош М., Бигалиева Ж.С., Байтурганова В.К., 20262026Кадеркеева З.К., Омарбекова А.С., Милош М., Бигалиева Ж.С., Байтурганова В.К.Kaderkeyeva Z.K., Omarbekova A.S., Milosz M., Bigaliyeva Z.S., Baiturganova V.K.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://tech.vestnik.shakarim.kz/jour/article/view/2199

В современную эпоху, когда быстрая трансформация ландшафтов знаний требует адаптивных механизмов обучения, интеграция искусственного интеллекта в образование больше не является футуристическим видением, а необходимостью. В этой статье представлена сложная интеллектуальная система, предназначенная для мониторинга, анализа и оптимизации приобретения компетенций на протяжении всего процесса обучения. Построенная на архитектуре нейронной сети, дополненной онтологическим моделированием и теоретико-множественными принципами, эта система обеспечивает структурированную, но гибкую основу для непрерывного совершенствования обучения. Используя методологию Six Sigma DMAIC (Определение-измерение-Анализ-Улучшение-контроль), предлагаемая модель систематически совершенствует образовательные траектории посредством анализа на основе данных и итеративных улучшений, обеспечивая точное соответствие отраслевым и институциональным требованиям.

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In the modern era, where rapidly changing educational landscapes require adaptive learning mechanisms, integrating artificial intelligence into education is no longer a futuristic dream but a necessity. This paper presents a sophisticated intelligent system for real-time monitoring, detailed analyzing, and adaptive optimizing of competency acquisition throughout the learning process. Based on a neural network architecture augmented with ontological modeling and set-theoretic principles, this system provides a structured yet flexible framework for continuous learning improvement. Using the Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) methodology, the proposed model systematically improves educational trajectories through data-driven analysis and iterative improvements, ensuring precise alignment with industry and institutional requirements. In addition, the system incorporates predictive analytics and personalized feedback mechanisms that adapt instructional strategies to individual learner needs, thus bridging the gap between standardized curricula and personal learning paths. It further enhances decision-making for educators by providing actionable insights, real-time performance dashboards, and evidence-based recommendations. By combining advanced computational intelligence with proven educational methodologies, this research contributes to the creation of resilient, scalable, and future-ready learning environments that foster innovation, efficiency, and lifelong skill development.

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The authors declare that there are no conflicts of interest present.