Comparative analysis of Russian and foreign generative neural networks for personalization of learning using English language teaching as an example
- Authors: Vlasov R.A.1
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Affiliations:
- Moscow City University
- Issue: Vol 22, No 2 (2025)
- Pages: 233-246
- Section: EVOLUTION OF TEACHING AND LEARNING THROUGH TECHNOLOGY
- URL: https://journals.rcsi.science/2312-8631/article/view/321338
- DOI: https://doi.org/10.22363/2312-8631-2025-22-2-233-246
- EDN: https://elibrary.ru/EKHYWL
- ID: 321338
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Abstract
Problem statement. Innovative technologies, such as generative adversarial networks (GANs), can significantly improve the quality of education and interest schoolchildren in learning foreign languages. GANs generate new content: texts, images, videos and sounds. This can increase the efficiency of the educational process, develop creative thinking, and make the learning process more personalized, which will meet modern educational trends. In Russia, support for the introduction of artificial intelligence in education comes from the government, but so far the domestic market of educational applications based on GANs is poorly developed, despite the competitiveness of our technologies. There are many more similar developments abroad, but not all of them are suitable for implementation in the educational process in Russian schools. Methodology . The study analyzed Russian and foreign programs, applications and services based on generative neural networks that have already been implemented or that can be implemented in the educational process of a foreign language. Results . Among foreign developments, Duolingo, Squirrel AI, Grammarly, Twee, etc. stand out. In Russia, there are no services based on generative neural networks specifically created for the education sector, but there are analogues of ChatGPT and MidJourney - YandexGPT, Shedevroom, GigaChat and Kandinsky, which can be used to create educational materials in a foreign language. Conclusion . Domestic developments, such as YandexGPT and GigaChat, open up new horizons for Russian education. They allow you to create personalized curricula and educational materials that can take into account the individual characteristics of each student, which increases the effectiveness of learning and motivation to study the material. In addition, generative neural networks simplify the routine tasks of teachers, freeing up time for creative interaction with schoolchildren. GANs also develop the skills of independent thinking and a creative approach to problem solving. The integration of these technologies into educational processes requires cooperation between developers, teachers and students, which will create innovative and adaptive environments for the successful development of students.
About the authors
Roman A. Vlasov
Moscow City University
Author for correspondence.
Email: vlasov.roman99@mail.ru
ORCID iD: 0009-0003-0243-1031
SPIN-code: 4584-5500
postgraduate student, Department of Informatization of Education, Institute of Digital Education
29 Sheremetyevskaya St, Moscow, 127521, Russian FederationReferences
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