The LUMIQа intelligent information system as a digital infrastructure for designing and applying adaptive methods of teaching natural science subjects

The paper presents LumiQa, a platform designed and developed by the author as an adaptive open electronic educational platform, as an intelligent information system and a digital infrastructure for designing and applying adaptive methods of teaching natural science subjects. The platform is interpreted as a pedagogical and technological circuit that combines local subject knowledge graphs, digital learning traces, retrieval-augmented generation (RAG), AI assistants, automated assessment, and instructor escalation in exceptional cases. The paper demonstrates the system through four screenshots: a Python course knowledge graph visualization, the laboratory submission assistant, the final test preparation assistant, and the modal test window. The methodological core is the cycle of diagnostics, analytics, and adaptation as a common logic for a set of adaptive methods. The reinforcement-learning recommender and interdisciplinary links between local subject knowledge graphs are described as elements of the platform development architecture that are under active development, integration, and methodological adjustmen by the grant team.

1. Xie, H., Chu, H. C., Hwang, G.-J., Wang, C. C. Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017 // Computers & Education. – 2019. – Vol. 140. – Art. 103599. – DOI: 10.1016/j.compedu.2019.103599.

2. Khor, E. T., Mutthulakshmi, K. A Systematic Review of the Role of Learning Analytics in Supporting Personalized Learning // Education Sciences. – 2024. – Vol. 14. – №. 1. – Art. 51. – DOI: 10.3390/educsci14010051.

3. Власова, Е. З., Власов, Д. В. Большие данные в анализе IT-успешности преподавателей университета // Известия Балтийской государственной академии рыбопромыслового флота: психолого-педагогические науки. – 2024. – № 1(67). – С. 7–12. – DOI:10.46845/2071-5331-2024-1-67-7-12. – URL: https://brarf.journal.klgtu.ru/jour/article/view/4 (дата обращения: 08.06.2026).

4. Власов, Д. В. Автоматизация диагностики знаний обучающихся средствами больших языковых моделей и облачных интеграционных платформ // Современные информационные технологии и ИТ-образование. – 2026. – Т. 21. – № 4. – URL: http://sitito.cs.msu.ru/index.php/SITITO/article/view/1279 (дата обращения: 08.07.2026).

5. Власов, Д. В. Автоматизация тестирования обучающихся средствами генеративного искусственного интеллекта и современных интеграционных платформ // Педагогика информатики. – 2025. – № 1-2. – С. 28–46. – URL: https://pcs.bsu.by/2025_1-2/3ru.pdf (дата обращения: 08.06.2026).

6. Власов, Д. В. Средства генеративного искусственного интеллекта и современных интеграционных платформ: новое поколение систем тестирования учебных достижений, обучающихся // Инноватика в профессиональном образовании: технологический подход / под науч. ред. И. Д. Рудинского, Е. З. Власовой. – Москва: Горячая линия – Телеком, 2026. – С. 200–218. – ISBN 978-5-9912-1191-8. –URL: https://www.techbook.ru/book.php?id_book=1442 (дата обращения: 08.06.2026).

7. Abu-Salih, B., Alotaibi, S. A systematic literature review of knowledge graph construction and application in education // Heliyon. – 2024. – Vol. 10. – №. 3. – Art. e25383. – DOI: 10.1016/j.heliyon.2024.e25383.

8. Li, A., Li, Y., Gao, X. Personalized Learning Path Recommendation Based on Knowledge Graphs: A Survey // Electronics. – 2026. – Vol. 15. – №. 1. – Art. 238. – DOI: 10.3390/electronics15010238.

9. Soumya, M. D., Krishnamoorthy, S. Knowledge Graphs for Representing Knowledge Progression of Students across Heterogeneous Learning Systems // International Journal of Artificial Intelligence in Education. – 2025. – Vol. 35. – №. 4. – Pр. 1695–1723. – DOI: 10.1007/s40593-024-00434-w.

10. Бойцова, Т. Б., Власова, Е. З., Горбунова, В. В., Исаева, Е. И. Междисциплинарная интеграция химии и информатики при реализации образовательной программы двухпрофильного бакалавриата в Герценовском университете // Современные проблемы науки и образования. – 2026. – № 2. – Ст. 23. – DOI: 10.17513/spno.34456. – URL: https://s.science-education.ru/pdf/2026/2/34456.pdf (дата обращения: 08.07.2026).

11. Li, Z., Wang, Z., Wang, W., Hung, K., Xie, H., Wang, F. L. Retrieval-augmented generation for educational application: A systematic survey // Computers and Education: Artificial Intelligence. – 2025. – Vol. 8. – Art. 100417. – DOI: 10.1016/j.caeai.2025.100417.

12. Wang, H., Tlili, A., Huang, R., Cai, Z., Li, M., Cheng Z. Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective // Education and Information Technologies. – 2023. – Vol. 28. – №. 7. – Pр. 9113–9148. – DOI: 10.1007/s10639-022-11555-x.

13. Memarian, B., Doleck, T. Human-in-the-loop in artificial intelligence in education: A review and entity-relationship analysis // Computers in Human Behavior: Artificial Humans. – 2024. – Vol. 2. – №. 1. – Art. 100053. – DOI: 10.1016/j.chbah.2024.100053.

14. Bikanga Ada M. Using design-based research to develop a Mobile Learning Framework for Assessment Feedback // Research and Practice in Technology Enhanced Learning. – 2018. – Vol. 13. – Art. 3. – DOI: 10.1186/s41039-018-0070-3.

15. Ruan, S., Lu, K. Adaptive deep reinforcement learning for personalized learning pathways: A multimodal data-driven approach with real-time feedback optimization // Computers and Education: Artificial Intelligence. – 2025. – Vol. 9. – Art. 100463. – DOI: 10.1016/j.caeai.2025.100463.