The pedagogical potential of generative artificial intelligence in the formation of linguo-mathematical competence

   Introduction.

   The relevance of the study is driven by the growing number of international students in Russian universities and the need to develop their linguo-mathematical competence – the ability to verbalize and interpret mathematical formulas and program code in Russian.

   Traditional methods of teaching Russian as a foreign language often lack the necessary flexibility for practicing these highly specialized skills.

   Purpose setting.

   The aim of the work is to develop a methodology for using generative artificial intelligence for the targeted formation of linguo-mathematical competence among international students in technical fields.

   Methodology and methods of the study. The study is based on a comparative analysis of the results of generating educational materials by three language models (GigaChat, YandexGPT, DeepSeek) using a typology of prompts developed by the authors (generator prompts, analyzer prompts, scenario constructor prompts) and principles of prompt engineering. Content quality assessment was conducted using expert analysis.

   Results. A comprehensive typology of educational prompts has been developed and tested. The strengths and weaknesses of the language models were identified: YandexGPT demonstrates structural rigor, DeepSeek shows an orientation towards comprehensive communication development, while GigaChat yielded the least satisfactory results. Strategies for refining prompts to minimize errors were determined.

   Conclusion. It is proven that targeted prompt engineering transforms generative AI into an effective tool for creating personalized educational materials that bridge the gap between language knowledge and its application in the specialty. The teacher receives an effective method for the rapid generation of contextually relevant tasks.

1. Manaeva E. N. Pedagogical conditions for the formation of mathematical language competence of a future teacher. The world of science. Pedagogy and psychology, 2024, vol. 12, no. 2, pp. 34–47. (In Russ.)

2. Kong S. K., Yang Y. A human-centered learning system using generative artificial intelligence for self-regulated learning development through the study of a subject area in K-12 conditions. IEEE transactions on Learning Technologies, 2024, no. 17, pp. 1588–1599. DOI: 10.1109/TLT.2024.3392830

3. Lai J. W. Adaptation of self-regulated learning in the era of chatbots with generative artificial intelligence. The Internet of the future, 2024, no. 16 (6), p. 218. DOI: 10.3390/fi16060218

4. Avraamenko I. V. Russian lessons for foreigners: techniques and possibilities of generative AI. Bulletin of Linguodidactics, 2024, vol. 10, no. 2, pp. 45–59.

5. Dzyuba E. V. Russian lessons for foreigners: artificial intelligence techniques or the art of technology? Russian studies, 2024, no. 2, pp. 242–261. (In Russ.)

6. Bogatova S. M., Frese O. V. Didactic possibilities of neural networks in teaching foreign languages. Modern pedagogical education, 2024, no. 3, pp. 187–192. (In Russ.)

7. Vovk E. V. Methods of artificial intelligence in the educational process of higher education. Problems of modern pedagogical education, 2022, no. 77–1, pp. 109–112. (In Russ.)

8. Dolinsky M. S. Directions of using generative artificial intelligence in initial programming training at universities. KIO, 2024, no. 2, pp. 85–96. (In Russ.)

9. Rayskaya L. K., Lambovskaya M. R. Prospects for the use of CHATGPT for higher education : an overview of international studies. Information Technology. technologies in education and science, 2024, no. 1, pp. 10–21. (In Russ.)

10. Sokolova M. E. CHATGPT and prom engineering: on the introduction of genetic neural networks into science. Scientific research, 2024, no. 1, pp. 92–109. (In Russ.)

11. Semenkina I. A., Prusakova P. V. The use of artificial intelligence tools in teaching a foreign language : a theoretical review. Philological Sciences. Questions of theory and practice, 2025, Vol. 18, no. 1, pp. 384–392. (In Russ.)

12. Sysoev P. V., Filatov E. M. Artificial intelligence technologies in teaching Russian as a foreign language. Rusistika, 2024, no. 2, pp. 300–317. (In Russ.)

13. Kozlovtseva N. A. The potential of using VR technologies in teaching Russian as a foreign language. Municipal education: innovations and experiment, 2024, no. 1 (104), pp. 65–67. (In Russ.)

14. Shumutova T. N. Language for spies (LSP): questions of theory. West Ural State University. Series: Linguistics, 2007, no. 15 (87), pp. 55–62. (In Russ.)

15. Khakimov H. I. Cognitive linguistics: an approach to language as an integral cognitive phenomenon. Bulletin of Science and Education, 2022, no. 6–2 (126), pp. 40–43. (In Russ.)

16. Alferyeva-Termsikos V. B. Industrial engineering as a strategy for the formation of students’ information culture. International Journal of Humanities and Natural Sciences, 2024, no. 9–1 (96), pp. 10–15. (In Russ.)

17. Hartley K., Hayak M., Ko U. H. Artificial intelligence supporting students’ independent learning: a practical example of using chatGPT and teaching programming. Education Sciences (Basel), 2024, no. 14 (2), p. 120. DOI: 10.3390/educsci14020120

18. Kilde L. Integration of generative AI into the education of a foreign language teacher : a systematic review of the literature. Pedagogy, 2024, no. 154 (2), pp. 5–26. DOI: 10.15823/p.2024.154.1

19. Li H. Y., Chen P. H., Wang V. S., Huang Y. M., Wu T. T. Enhancing communication opportunities through a leadership mechanism in blended learning: the effect of self-regulated learning, higher-order thinking skills, and knowledge formation. International Journal of Educational Technologies in Higher Education, 2024, no. 21 (1). URL: https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-024-00447-4 doi: 10.1186/s41239-024-00447-4 (accessed 09. 17. 2025).

20. Suriano R., Plebe A., Acciai A., Fabio R. A. Student interaction with ChatGPT can contribute to the development of complex critical thinking skills. Training and instruction, 2025, No. 95. URL: https://www.sciencedirect.com/science/article/abs/pii/S0959475224001385 doi: 10.1016/j.learninstruc.2024.102011 (accessed: 09/17/2025).

21. Tan K. S., Cooper G., Rappa N., Cooper M., Sims S., Nonis K. A dialogical approach to transforming teaching, learning, and Assessment through generative artificial Intelligence in secondary education: Proof of concept. Pedagogy: An International Journal, 2024, no. 19 (3), pp. 493–503. DOI: 10.1080/1554480X.2024.2379774