Применение искусственного интеллекта для персонализации обучения студентов с низким уровнем учебной вовлечённости

Maxim S. Krasnik; Ruslan R. Salakhetdinov; Tamari M. Rogozhkina

doi:10.25726/s5361-5222-0444-v

Authors

Maxim S. Krasnik https://orcid.org/0009-0000-0867-9722 Kozma Minin Nizhny Novgorod State Pedagogical University (Minin University), 603950, Nizhny Novgorod, Ulyanova St., 1
Ruslan R. Salakhetdinov https://orcid.org/0009-0000-1932-648X Kozma Minin Nizhny Novgorod State Pedagogical University (Minin University), 603950, Nizhny Novgorod, Ulyanova St., 1
Tamari M. Rogozhkina https://orcid.org/0009-0004-0224-8082 Kozma Minin Nizhny Novgorod State Pedagogical University (Minin University), 603950, Nizhny Novgorod, Ulyanova St., 1

DOI: 10.25726/s5361-5222-0444-v EDN: HLINAN

How to cite

GOST Krasnik M. S., Salakhetdinov R. R., Rogozhkina T. M. Application of artificial intelligence for the personalization of learning of students with a low level of academic engagement // Education Management Review. 2026. Vol. 16. No. 8. P. 155-169. DOI: 10.25726/s5361-5222-0444-v

APA Krasnik, M. S., Salakhetdinov, R. R. & Rogozhkina, T. M. (2026). Application of artificial intelligence for the personalization of learning of students with a low level of academic engagement. Education Management Review, 16(8), 155-169. https://doi.org/10.25726/s5361-5222-0444-v

Abstract

The article examines the problem of low academic engagement of students in a digital educational environment. It is shown that the mere presence of digital tools does not guarantee a student's active participation in learning, while the traditional signs of declining activity become less noticeable in distance and blended formats. The necessity of applying artificial intelligence is substantiated not as a means of automatically replacing the teacher, but as an instrument of the pedagogical personalization of learning. Academic engagement is considered as a multicomponent phenomenon that includes behavioural, cognitive and emotional components, which are partially reflected in the student's digital trace. A structural model is proposed that includes the diagnostics of the digital trace, the interpretation of signs of declining engagement, the personalization of learning tasks, targeted feedback and repeated monitoring. A projected diagnostic indicator of engagement, an approbation framework of the model and criteria for its expert assessment are presented separately: diagnostic applicability, pedagogical correctness, feasibility, transparency and ethical safety. It is emphasized that the leading role in making pedagogical decisions remains with the teacher, while the model itself is theoretical and requires further expert and empirical verification.

Keywords

artificial intelligence personalization of learning academic engagement digital educational environment digital trace feedback students expert assessment

Funding

The authors did not declare any external funding for this research.

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Application of artificial intelligence for the personalization of learning of students with a low level of academic engagement

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