Science for Education Today, 2026, vol. 16, no. 2, pp. 264–300
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Developing professionally oriented communicative competence: Evaluating the factors of integrating large language models into foreign language instruction of non-linguistic university students

Yarovikova Y. V. 1 (Moscow, Russian Federation), Balygina E. A. 2 (Moscow, Russian Federation), Logvinova O. K. 2 (Moscow, Russian Federation)
1 Moscow State University of Psychology & Education
2 Moscow State University of Psychology and Education
Abstract: 

Introduction. The main research problem of the article lies in the key contradiction between the high technological potential of ChatGPT for foreign language teaching and learning and the significant risks associated with its integration, especially in the context of non-linguistic universities. Resolving this contradiction requires establishing a pedagogical balance through controlled interaction with artificial intelligence. This would allow to minimize associated risks and direct the potential of large language models like ChatGPT towards the development of professionally oriented communicative competence rather than abstract language skills.
The aim of this study is to identify, summarize, and comprehensively assess key factors affecting the integration of ChatGPT into foreign language instruction of non-linguistic university students. In order to achieve this aim, the study also included an analysis of both the restraining barriers related to the technology’s use and the conditions necessary for its pedagogically-based and effective integration into the educational process.
Materials and Methods. The research methodology was based on the synthesis of three complementary components: 1) conceptual (assessment of students’ perceptions of ChatGPT within the Technology Acceptance Model, TAM); 2) analytical (structuring the potential and risks of integration using SWOT analysis); 3) design-oriented (modeling an integration scenario based on the pedagogical strategy “Think–Talk–Write” (TTW). Among the major empirical methods employed were mathematical methods of statistics, which included the collection, analysis (both qualitative and quantitative), and interpretation of the data obtained from an anonymous survey of 113 students with varying language proficiency levels. During the study, the authors developed a specialized questionnaire, consisting of two blocks of closed-ended, semi closed-ended, and open-ended questions aimed at identifying behavioral patterns of ChatGPT usage and students’ subjective attitudes towards its integration into foreign language. The design of a potential integration scenario was carried out through conceptual modeling built on the theoretical analysis (TAM model, SWOT analysis) and the empirical survey (questionnaire).
Results. Based on empirical data, the following results were obtained: 1. Taking into account characteristic features and subjective assessment of interaction with ChatGPT for educational purposes, the study identified behavioral patterns of the technology’s usage and its perceptions among non-linguistic university students. 2. The TAM model revealed high perceived usefulness and ease of use of ChatGPT, as well as a number of ethical and psychological barriers hindering its full integration. 3. The SWOT analysis empirically confirmed a complex of factors promoting or restraining the integration of ChatGPT into foreign language instruction of non-linguistic university students. 4. In response to the key contradiction between the enormous practical potential of the technology for solving specific learning tasks and its uncritical use, a pedagogical model of integration based on the TTW strategy was developed. 5. Conditions for implementing this model were determined, which are driven by the need to address primary language learning problems in non-linguistic universities; a specific mechanism for overcoming these problems through controlled integration of artificial intelligence into blended learning was proposed.
Conclusions. The study concludes that the successful integration of ChatGPT requires the development of conceptually new models that would balance the technology’s potential with the necessity of strict pedagogical control. The theoretically and empirically justified model proposed in this study not only acknowledges the need for such control but also provides an embedded mechanism for its implementation within the educational process. The model demonstrates that overcoming risks of passivity, superficial learning, and academic dishonesty of non-linguistic university students is possible not through abandoning the technology but through its controlled integration into blended learning structure.
According to the authors, such balanced yet controllable interaction with ChatGPT opens the way to risk reduction and the unlocking of the technology’s potential, thereby ensuring a high-quality digital transformation of education.

Keywords: 

Digital transformation of education; Artificial intelligence; Large language models; Factors of integration; Model of integration; Controlled integration; Blended learning; Foreign language instruction; Non-linguistic university.

For citation:
Yarovikova Y. V., Balygina E. A., Logvinova O. K. Developing professionally oriented communicative competence: Evaluating the factors of integrating large language models into foreign language instruction of non-linguistic university students. Science for Education Today, 2026, vol. 16, no. 2, pp. 264–300. DOI: http://dx.doi.org/10.15293/2658-6762.2602.12
References: 
  1. Sysoyev P. V., Filatov E. M. Chatbots in teaching a foreign language: Advantages and controversial issues. Tambov University Review. Series: Humanities, 2023, vol. 28 (1), pp. 66-72. (In Russian) URL: https://elibrary.ru/PXGZTJ DOI: https://doi.org/10.20310/1810-0201-2023-28-1-66-72    
  2. Hwang G. J., Chang, C. Y. A review of opportunities and challenges of chatbots in education. Interactive Learning Environments, 2021, vol. 31 (7), pp. 4099-4112. DOI: https://doi.org/10.1080/10494820.2021.1952615
  3. Ivakhnenko E. N., Nikolskiy V. S. ChatGPT in higher education and science: A threat or a valuable resource? Higher Education in Russia, 2023, vol. 32 (4), pp. 9-22. (In Russian) URL: https://elibrary.ru/TZHIHU DOI: https://doi.org/10.31992/0869-3617-2023-32-4-9-22
  4. Lavrinenko I. Yu. The ChatGPT use in the English language teaching process in a non-language university: Theoretical aspect. Herald of Siberian Institute of Business and Information Technologies, 2023, vol. 12 (2), pp. 18-25. (In Russian) URL: https://elibrary.ru/UIAZUW
  5. Sysoyev P. V., Filatov E. M. Artificial intelligence in teaching Russian as a foreign language. Russian Language Studies, 2024, no. 2, pp. 300-317. (In Russian) URL: https://elibrary.ru/SOHSKZ DOI: https://doi.org/10.22363/2618-8163-2024-22-2-300-317
  6. Arzyutova S. N. ChatGPT using in English language teaching. Humanitarian Studies. Pedagogy and Psychology, 2023, no. 16, pp. 37–45. (In Russian)  URL: https://elibrary.ru/QQXVED   
  7. Farrokhnia M., Banihashem S. K., Noroozi O., Wals A.   A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 2023, vol. 61 (3), pp. 460-474. DOI: https://doi.org/10.1080/14703297.2023.2195846
  8. Cotton D. R., Cotton P. A., Shipway J. R. Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International,   2024, vol. 61 (2), pp. 228-239. DOI: https://doi.org/10.1080/14703297.2023.2190148
  9. Garkusha N. S., Gorodova J. S. Pedagogical opportunities of ChatGPT for developing cognitive activity of students. Vocational Education and Labour Market, 2023, vol. 11 (1), pp. 6-23. (In Russian) URL: https://elibrary.ru/NBBIRG DOI: https://doi.org/10.52944/PORT.2023.52.1.001

10.Bermus A. G. Benefits and risks of using ChatGPT in higher education: A theoretical review. Pedagogy. Theory & Practice, 2024, vol. 9 (8), pp. 776-787. (In Russian) URL: https://elibrary.ru/DPYUDU DOI: https://doi.org/10.30853/ped20240099

11.Sok S., Heng K. ChatGPT for education and research: A review of benefits and risks. Cambodian Journal of Educational Research, 2023, vol. 3 (1), pp. 110-121. DOI: https://doi.org/10.62037/cjer.2023.03.01.06

12.Baskara R., Mukarto M. Exploring the implications of ChatGPT for language learning in higher education. Indonesian Journal of English Language Teaching and Applied Linguistics, 2023, vol. 7 (2), pp. 343-358. DOI: http://dx.doi.org/10.21093/ijeltal.v7i2.1387

13.Fryer L. K., Ainley M., Thompson A., Gibson A., Sherlock Z. Stimulating and sustaining interest in a language course: An experimental comparison of chatbot and human task partners. Computers in Human Behavior, 2017, vol. 75, pp. 461-468. DOI: https://doi.org/10.1016/j.chb.2017.05.045

14.Kim H., Cha Y., Kim Na Y. Effects of AI chatbots on EFL students’ communication skills. Korean Journal of English Language and Linguistics, 2021, vol. 21, pp. 712-734. URL: http://journal.kasell.or.kr/xml/30253/30253.pdf

15.Solak E. Revolutionizing language learning: How ChatGPT and AI are changing the way we learn languages. International Journal of Technology in Education, 2024, vol. 7 (2), pp. 353-372. DOI: https://doi.org/10.46328/ijte.732

16.Kudryashova S. V. The role of artificial intelligence in language education (the case of legal French). Perm National Research Polytechnic University Linguistics and Pedagogy Bulletin, 2024, no. 3, pp. 68-80. (In Russian) URL: https://elibrary.ru/ELLCDD

17.Petruneva R. M., Filatova M. N., Chudasova T. D. Electronic information and educational environment in higher education institution: Current state (on the example of VSTU). Primo Aspectu, 2024, vol. 2 (58), pp. 19-31. (In Russian) URL: https://elibrary.ru/QKUGXQ DOI: https://doi.org/10.35211/2500-2635-2024-2-58-19-31

18.Vidova T. A., Romanova I. N. The opportunities of using artificial intelligence technologies in the educational process. Educational Resources and Technologies, 2023, vol. 42 (1), pp. 27-35. (In Russian) URL: https://elibrary.ru/DYOKHP

19.Bezgodova S. A., Miklyaeva A. V. Digital academic dishonesty: A socio-psychological analysis. Science for Education Today,2021, no. 4, pp. 64-90. (In Russian) URL: https://elibrary.ru/TCQVMQ DOI: http://dx.doi.org/10.15293/2658-6762.2104.04

20.Shiri A. ChatGPT and academic integrity. Information Matters, 2023, vol. 3 (2), pp. 1-5. DOI: http://dx.doi.org/10.2139/ssrn.4360052

21.Kuvshinova E. E. Application of artificial intelligence in teaching a foreign language. Humanities of the South of Russia, 2024, vol. 13 (2), pp. 75-84. (In Russian) URL: https://elibrary.ru/BDDVXH DOI: https://doi.org/10.18522/2227-8656.2024.2.7

22.Abbas M., Jam F. A.,   Khan T. I. Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 2024, vol. 21 (1), pp. 1-22. DOI: https://doi.org/10.1186/s41239-024-00444-7

23.Rakitov A. I. Higher education and artificial intelligence: Euphoria and alarmism. Higher Education in Russia, 2018, vol. 27 (6), pp. 41-49. (In Russian)  URL: https://elibrary.ru/USPQDV

24.Listiana L., Raharjo, Hamdani A. S. Enhancing self-regulation skills through group investigation integrated with think-talk-write. International Journal of Instruction, 2020, vol. 13 (1), pp. 915-930. DOI: https://doi.org/10.29333/iji.2020.13159a 

25.Listiana L., Rosyidah F., Daesusit R., Hamdani A. S. Fostering metacognitive skills and learning motivation through hybrid learning with innovative learning strategies. International Journal of Cognitive Research in Science, Engineering and Education, 2025, vol. 13 (1), pp. 335-348. DOI: https://doi.org/10.23947/2334-8496-2025-13-2-335-348

26.Qomariyah S. S., Nafisah B. Z. Examining think-talk-write (TTW) strategy in students’ vocabulary mastery. Journal of Languages and Language Teaching, 2020, vol. 8 (1), pp. 72-82. DOI:  https://doi.org/10.33394/jollt.v8i1.2240 

27.27. Andreeva N. V. Pedagogy of effective blended learning. Journal of Modern Foreign Psychology, 2020, vol. 9 (3), pp. 8-20. (In Russian) URL: https://www.elibrary.ru/ZEVSJK   DOI: https://doi.org/10.17759/jmfp.2020090301

28.Bahri A., Idris I. S., Muis H., Arifuddin M., Fikri M. J. N. Blended learning integrated with innovative learning strategy to improve self-regulated learning. International Journal of Instruction, 2021, vol. 14 (1), pp. 779-794. DOI: https://doi.org/10.29333/iji.2021.14147a

29.Grishaeva A. V. The use of blended learning in teaching foreign languages to students of non-linguistic specialties. Tomsk State Pedagogical University Bulletin. 2015, vol. 157 (4), pp. 70-74. (In Russian) URL: https://www.elibrary.ru/TRJYEB 

30.Lee H., Chen P., Wang W., Huang Y., Wu T. Empowering ChatGPT with guidance mechanism in blended learning: Effect of self-regulated learning, higher-order thinking skills, and knowledge construction. International Journal of Educational Technology in Higher Education, 2024, vol. 21 (1), pp. 1-28. DOI: https://doi.org/10.1186/s41239-024-00447-4

31.Lukichyov P. M., Chekmarev O. P. Risks of artificial intelligence in higher education. Russian Journal of Innovation Economics,2024,vol. 14 (2), pp. 463-482. (In Russian) URL: https://elibrary.ru/MKEVSE DOI: https://doi.org/10.18334/vinec.14.2.120731

32.Kalinichenko N. S., Velichkovsky B. B. The technology acceptance phenomenon: Current state and future research. Organizational Psychology, 2022, vol. 12 (1), pp. 128-152. (In Russian) URL: https://elibrary.ru/IYOMXC

33.Adams D. A., Nelson R. R., Todd P. A. Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 1992, vol. 16 (2), pp. 227-247.  DOI: https://doi.org/10.2307/249577

34.Al-Mamary H. Y., Al-Nashmi M., Hassan G. A. Y., Shamsuddin A. A Critical review of models and theories in field of individual acceptance. International Journal of Hybrid Information Technology, 2016, vol. 9 (6), pp. 143-158.DOI: http://dx.doi.org/10.14257/ijhit.2016.9.6.13

35.Rahman M. M., Lesch M. F., Horrey W. J., Strawderman L. Assessing the utility of TAM, TPB, and UTAUT for advanced driver acceptance systems. Accident Analysis and Prevention, 2017, vol. 108, pp. 361-373. DOI: https://doi.org/10.1016/j.aap.2017.09.011

36.Lin Y., Yu Z. Extending technology acceptance model to higher-education students’ use of digital academic reading tools on computers. International Journal of Educational Technology in Higher Education, 2023, vol. 20 (1), pp. 1-24. DOI: https://doi.org/10.1186/s41239-023-00403-8

37.Kasneci E., Sessler K., Küchemann S., Bannert M. ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 2023, vol. 103, pp. 102274. DOI: https://doi.org/10.1016/j.lindif.2023.102274

38.Budak Durmus F. Swot analysis of the use of ChatGPT in education. Journal of Educational Studies and Multidisciplinary Approaches, 2024, vol. 4 (2), pp. 121-137. DOI: https://doi.org/10.51383/jesma.2024.102

39.Thorp H. ChatGPT is fun, but not an author. Science, 2023, vol. 379 (6630), pp. 313. DOI: http://doi.org/10.1126/science.adg7879

40.Tian S., Huang S., Li R., Wei C. A prompt construction method for the reverse dictionary task of large-scale language models. Engineering Applications of Artificial Intelligence, 2024, vol. 133, pp. 108596. DOI: https://doi.org/10.1016/j.engappai.2024.108596

41.Kim J., Yu S., Lee S. S., Detrick R. Students’ prompt patterns and its effects in AI-assisted academic writing: Focusing on students’ level of AI literacy. Journal of Research on Technology in Education, 2025, pp. 1-18. DOI: https://doi.org/10.1080/15391523.2025.2456043

42.Sysoyev P. V., Filatov, E. M. ChatGPT in students’ research work: To forbid or to teach? Tambov University Review. Series: Humanities, 2023, vol. 28 (2), pp. 276-301. (In Russian) URL: https://elibrary.ru/SPHXKZ DOI: https://doi.org/10.20310/1810-0201-2023-28-2-276-301

Date of the publication 30.04.2026