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Integration of artificial intelligence into blended learning: improving bilingual education in the Russian-Tatar context

https://doi.org/10.26907/2782-4756-2025-82-4-319-327

Abstract

The article studies the integration of artificial intelligence technologies into blended learning to improve bilingual education in the Russian-Tatar context. The relevance of the research is the need to overcome the digital inequality of languages in the educational space of multicultural regions of Russia. The paper identifies the specific challenges that students face when studying technical disciplines in a bilingual educational environment, including an imbalance of digital educational resources, academic inequality, and increased cognitive load. Based on system analysis and pedagogical forecasting methods, a comprehensive model for using AI tools to create an adaptive educational ecosystem has been developed. The main focus is on developing adaptive learning methods that take into account the individual language profile of each student. The practical significance of the work is the creation of an integrated system that makes it possible to bridge the existing gap between technological progress and the real needs of bilingual education. The developed model can be adapted for other regions of Russia with similar educational challenges.

About the Author

L. Mukhametshin
Kazan Federal University
Russian Federation

Mukhametshin Lenar Minnekhanovich, Assistant Professor

18 Kremlyovskaya Str., Kazan, 420008



References

1. Staker, H., Horn, M. B. (2012). Classifying K-12 Blended Learning. R. 1. URL: http://www.christenseninstitute.org/wp-content/uploads/2013/04/Classifying-K-12-blended-learning.pdf (accessed: 28.11.2025). (In English)

2. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science. Vol. 12. No. 2, pp. 257–285. https://doi.org/10.1207/s15516709cog1202_4. (In English)

3. Moiseeva, N. A., Polyakova, T. A. (2024). Tekhnologii smeshannogo obucheniya v prepodavanii distsiplin informatsionno-matematicheskogo tsikla v tekhnicheskom vuze [Blended Learning Technologies in Teaching Computer Science and Mathematical Disciplines at a Technical University]. Nauchno-metodicheskii elektronnyi zhurnal “Kontsept”. No. 11, pp. 130–149. URL: https://e-koncept.ru/2024/241182.htm (accessed: 28.11.2025). (In Russian)

4. Godwin-Jones, R. (2021). Evolving Technologies for Language Learning. Language Learning & Technology. Vol. 25. No. 3, pp. 6–26. DOI:10.64152/10125/73443. (In English)

5. Cummins, Jim. (2021). Rethinking the Education of Multilingual Learners: A Critical Analysis of Theoretical Concepts. 464 p. Bristol, Blue Ridge Summit: Multilingual Matters. https://doi.org/10.21832/9781800413597. URL: https://www.multilingual-matters.com/page/detail/?K=9781800413573 (accessed: 28.11.2025). (In English)

6. García, O., Wei, L. (2014). Translanguaging: Language, Bilingualism and Education. 165 p. Palgrave Macmillan. https://doi.org/10.1057/9781137385765. (In English)

7. Yang, T.-C., Hwang, G.-J., & Yang, S. J.-H. (2013). Development of an Adaptive Learning System with Multiple Perspectives Based on Students’ Learning Styles and Cognitive Styles. Educational Technology & Society. No. 16 (4), pp. 185–200. – URL: https://www.researchgate.net/publication/279764849_Development_of_an_Adaptive_Learning_System_with_Multiple_Perspectives_based_on_Students'_Learning_Styles_and_Cognitive_Styles (accessed: 28.11.2025). (In English)

8. Suleimanov, D. Sh., Gil'mullin, R. A., Gatiatullin, A. R. (2023). Sovremennye lingvisticheskie resursy i IT-razrabotki dlya tatarskogo yazyka: regional'nyi opyt [Modern Linguistic Resources and IT Developments for the Tatar Language: Regional Experience]. Altaistika. No. 3 (10), pp. 60–69. DOI: https://doi.org/10.25587/2782-6627-2023-3-60-69, https://www.altaisticsvfu.ru/jour/article/view/102/93. (In Russian)

9. Blinov, V. I., Esenina, E. Yu., Sergeev, I. S. (2021). Modeli smeshannogo obucheniya: organizacionno-didakticheskaya tipologiya [Blended Learning Models: Organizational and Didactic Typology]. Vysshee obrazovanie v Rossii. T. 30. No. 5, pp. 44–64. DOI: 10.31992/0869-3617-2021-30-5-44-64. (In Russian)

10. Pletyago, T. Yu., Ostapenko, A. S., Antonova, S. N. (2019). Pedagogicheskie modeli smeshannogo obucheniya v vuze: obobshchenie opyta rossiiskoi i zarubezhnoi praktiki [Pedagogical Models of Blended Learning in Higher Education: A Summary of Russian and International Experience]. Obrazovanie i nauka. T. 21, No. 5, pp. 113–130. DOI: 10.17853/1994-5639-2019-5-113-130. (In Russian)

11. Slovar' terminov i ponyatii tsifrovoi didaktiki (2021) [Dictionary of Terms and Concepts of Digital Didactics]. Ros. gos. prof.-ped. un-t; avt.-sost. N. V. Lomovtseva, K. M. Zarechneva, O. V. Ushakova, S. Yu. Yarina. 84 p. Ekaterinburg, RGPPU, Azhur. (In Russian)

12. Bonk, CJ. & Graham, CR. (2006). The Handbook of Blended Learning: Global Perspectives, Local Designs. 624 p. John Wiley, Sons Ltd. (In English)

13. Salekhova, L. L., Danilov, A. V., Zaripova, R. R., Fazliahmetov, T. R. (2024). Smeshannoe obuchenie: kompleksnyi analiz teoreticheskikh podkhodov i empiricheskikh issledovanii na osnove CABLS [Blended Learning: A Comprehensive Analysis of Theoretical Approaches and Empirical Research Based on CABLS]. Sovremennye naukoemkie tekhnologii. No. 11, pp. 231–236. DOI 10.17513/snt.40235. (In Russian)

14. VanLehn, K. (2011). The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems. Educational Psychologist. Vol. 46. No. 4, pp. 197–221. https://doi.org/10.1080/00461520.2011.611369. (In English)

15. Baker, R. S., Inventado, P. S. (2014). Educational Data Mining and Learning Analytics. In Learning Analytics: From Research to Practice. Pp. 61–75. DOI:10.1007/978-1-4614-3305-7_4, URL: https://www.researchgate.net/publication/278660799_Educational_Data_Mining_and_Learning_Analytics (accessed: 28.11.2025). (In English)

16. Yandex Translate API. Dokumentatsiya dlya razrabotchikov [Documentation for Developers]. URL: https://cloud.yandex.ru/docs/translate/ (accessed: 28.11.2025). (In Russian)

17. Yandex SpeechKit. Dokumentatsiya dlya razrabotchikov [Documentation for Developers]. URL: https://cloud.yandex.ru/docs/speechkit/(accessed: 28.11.2025). (In Russian)

18. Wang, Y., Han, X., Yang, J. (2015). Revisiting the Blended Learning Literature: Using a Complex Adaptive Systems Framework. Educational Technology & Society. Vol. 18, Issue 2, pp. 380–393. (In English)


Review

For citations:


Mukhametshin L. Integration of artificial intelligence into blended learning: improving bilingual education in the Russian-Tatar context. Philology and Culture. 2025;(4):319-327. (In Russ.) https://doi.org/10.26907/2782-4756-2025-82-4-319-327

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ISSN 2782-4756 (Print)