Intelligent Recommender Systems in Education. Algorithms for Personalised Learning Pathways

The digital transformation of education has led to exponential growth in learning activity data, creating technological prerequisites for the implementation of intelligent recommender systems (IRS). However, commercial recommendation algorithms optimised for maximising engagement and profit cannot be directly transferred to the educational environment, where the main objective function is to improve the quality of knowledge acquisition and reduce academic failure. Aim. To develop a conceptual model of an intelligent recommender system with explainable artificial intelligence mechanisms and built-in ethical control tools. Methods. The research is based on a systematic review of peer-reviewed for 2021–2025 indexed in Scopus, Web of Science, and RSCI. Methods of comparative analysis of recommendation algorithms, architectural modelling, and experimental validation on open educational datasets. Results. A four‑level IRS architecture is proposed, comprising a data collection layer, a processing and vectorisation layer, an algorithmic core based on a hybrid model, and an interaction layer with a recommendation explanation module. Experiments have shown that the proposed hybrid model outperforms classical collaborative filtering by 13% in precision and solves the cold‑start problem through semantic analysis of new courses. The developed explanation module, based on the LIME method, visualises the factors influencing each recommendation, thereby increasing trust among teachers and students. Conclusions. The results are consistent with the findings of leading international researchers on the advantages of hybrid graph models over purely collaborative approaches. Unlike known solutions, the proposed model includes a built‑in fairness checking block and is designed for integration with Russian learning management systems. Keywords: intelligent recommender systems; learning personalization; individual learning trajectory; hybrid algorithms; graph neural networks; learning analytics; explainable artificial intelligence

1. Bond M., Khosravi H., De Laat M., Bergdahl N. A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education. 2024;21:4. URL: https://doi.org/10.1186/s41239-023-00436-z

2. Holmes W., Bialik M., Fadel C. Artificial intelligence in education. In: Data ethics: Building trust: How digital technologies can serve humanity. 2023:621–653. URL: https://doi.org/10.58863/20.500.12424/4276068

3. Klašnja-Milićević A., Ivanović M., Vesin B., Satratzemi M., Wasson B. Learning Analytics — Trends and Challenges. Frontiers in Artifi Intelligence. 2022;5:856807. URL: https://doi.org/10.3389/frai.2022.856807

4. Duan C., Yang J., Cui Q., Zhang W., Wan X., Zhang M. Enhancing the recommendation of learning resources for learners via an advanced knowledge graph. Applied Sciences. 2025;15(8):4204. URL: https://doi.org/10.3390/app15084204

5. Rigou A., Kyriazi F., Thomakos D. Predicting student success through learning analytics: A comparative modeling approach. Creative Education. 2025;16:1107–1119. URL: https://doi.org/10.4236/ce.2025.168069

6. Wu S., Sun F., Zhang W., Xie X., Cui B. Graph Neural Networks in Recommender Systems: A Survey. ACM Computing Surveys. 2022;55(5):97. URL: https://doi.org/10.1145/3535101

7. Yan J., Liu S., Armwood-Gordon C., Li L. Factors Affecting Active Flipped Learning on Underrepresented Students in Three STEM Courses. Education and Information Technologies. 2024;29(9):10791–10804. URL: https://doi.org/10.1007/s10639-023-12234-1

8. Zakharova Zh.L., Zhirkova Z.S. Conditions for creating digital transformation in the educational environment of a university. In: Young Researcher: Issues of science and education. Kirov; 2021:282-285. (In Russ.). URL: https://www.elibrary.ru/pfwwoi

9. Holstein K., Aleven V., Rummel N. A Conceptual Framework for Human – AI Hybrid Adaptivity in Education. In: Artificial Intelligence in Education. Part I. Cham: Springer; 2020:240–254. URL: https://doi.org/10.1007/978-3-03052237-7_20

10. Ovsyannikova A.V. Digital transformation of education: key drivers, barriers and development prospects. The World of Science. Pedagogy and Psychology. 2025;13(3):27. (In Russ.). URL: https://www.elibrary.ru/velqya

11. Podlevskikh A.P., Suetin S.N., Stepkin S.V. Methodology for the course “Designing information systems” in the context of transition to a digital educational environment. Modern High-tech Technologies. 2021;2:45–51. (In Russ.). URL: https://doi.org/10.17513/snt.38492

12. Nguyen A., Ilesanmi F., Dang B., Vuorenmaa E., Järvelä S. Hybrid Intelligence in Academic Writing: Examining Self-Regulated Learning Patterns in an AI-Assisted Writing Task. In.: Hybrid Human Artifi Intelligence (HHAI). 2024;241-254. URL: https://doi.org/10.3233/FAIA240198.

13. Topali P., Ortega-Arranz A., Rodríguez-Triana M.J., Er E., Khalil M., Akçapınar G. Designing human-centered learning analytics and artifi intelligence in education solutions: A systematic literature review. Behaviour and Information Technology. 2025;44(5):1071–1098. URL: https://doi.org/10.1080/0144929X.2024.2345295

14. Bai Y., et al. Impact of generative AI interaction and output quality on university students‘ learning outcomes: A technology-mediated and motivation-driven approach. Scientifi Reports. 2025;15(1):24054. URL: https://doi.org/10.1038/s41598-025-08697-6

15. Hummel S. Ethical and Responsible AI in Education: Situated Ethics for Democratic Learning. Education Sciences. 2025;15(12):1594. URL: https://doi.org/10.3390/educsci15121594

16. Cannon O.V., Pustoshilo P.V., Ivantsov A.A. The problem of using neural networks and artifi intelligence tools by university students: An ethical aspect. Russian Humanitarian Journal. 2025;16(3):192-210. (In Russ.). URL: https://doi.org/10.12731/2658-4034-2025-16-3-862