Applications of large language models for digital educational platforms
https://doi.org/10.25682/NIISI.2025.2.0001
Abstract
This paper explores the potential of large language models (LLMs) to enhance interactions between students and educators within digital educational platforms. It analyzes modern state-of-the-art solutions – such as YandexGPT, Mistral, Qwen, LLaMA, and their variants – along with their architectural features, performance, and adaptability for educational tasks. The study demonstrates that proper model parameter tuning enables their effective use in automating routine tasks, personalizing learning, and expanding instructors' toolsets.
About the Authors
A. G. Leonov
НИЦ «Курчатовский институт» - НИИСИ, МГУ им. М. В. Ломоносова, МПГУ, Государственный университет управления
Russian Federation
Russian Federation
N. S. Martynov
НИЦ «Курчатовский институт» - НИИСИ
Russian Federation
Russian Federation
K. A. Mashchenko
НИЦ «Курчатовский институт» - НИИСИ, МГУ им. М. В. Ломоносова
Russian Federation
Russian Federation
M. S. Paremuzov
НИЦ «Курчатовский институт» - НИИСИ
Russian Federation
Russian Federation
K. K. Pchelin
НИЦ «Курчатовский институт» - НИИСИ
Russian Federation
Russian Federation
A. V. Shlyakhov
НИЦ «Курчатовский институт» - НИИСИ
Russian Federation
Russian Federation
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Review
For citations:
Leonov A.G., Martynov N.S., Mashchenko K.A., Paremuzov M.S., Pchelin K.K., Shlyakhov A.V. Applications of large language models for digital educational platforms. SRISA Proceedings. 2025;15(2):9-15. (In Russ.) https://doi.org/10.25682/NIISI.2025.2.0001
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