Students’ writing skills’ development based on the use of technological solutions based on artificial intelligence

Importance. The active introduction of artificial intelligence tools and technologies into all spheres of human life is significantly transforming the traditional methods of teaching foreign languages. There is an emerging pedagogical triad “teacher – artificial intelligence – learner”, where neural networks act as an active third party in the process. Artificial intelligence automates routine operations, helps with the search for materials, data processing, visualization, formatting of sources and evaluation of texts, thereby freeing up time for tasks requiring deeper human thinking. Neural networks provide instant feedback from educational and social to conditionally creative. Their integration is particularly effective for developing students’ writing skills, including writing essays in a foreign language.

Materials and Methods. The study involved students (N = 48) of the Russian Presidential Academy of National Economy and Public Administration in the field of preparation 41.03.01 “Foreign Regional Studies”. To test the effectiveness of the author’ methodology, seven stages of learning are developed as part of the work: 1) setting goals and objectives; 2) explaining the rules of author ethics; 3) interacting with an artificial intelligence tool; 4) checking the text with a neural network; 5) finalizing the text; 6) discussing the results of interaction in small groups; 7) verifying the text of the teacher’s work).

Results and Discussion. According to the experiment results, it is revealed that the author’s method of developing students’ writing skills based on technological solutions based on artificial intelligence had a positive effect on mastering active vocabulary (t = 2.3 at p = 0.015), the structure of essay types (t = 2.46 at p = 0.01) and the content of the work (t = 3.71 at p = 0.0005). In terms of the grammatical design of the text, a comparative analysis of the data did not reveal any differences between the samples (t = 1 at p = 0.16), which is explained by the students' possession of the necessary grammar knowledge at a high level before the experiment.

Conclusion. The novelty of this research lies in of a step-by-step methodology development for improving writing skills in students of higher educational institutions. The results obtained can be used in future research in the formation of individual writing skills such as grammar, spelling, argumentation, work plan creation, conclusions using artificial intelligence tools. 

1. Sysoyev P.V., Filatov E.M. (2024). Cognitive-matrix analysis as a tool for exploring cultural identity of a fiction author. Vestnik Moskovskogo universiteta. Seriya 19. Lingvistika i mezhkul’turnaya kommunikatsiya = Moscow State University Bulletin. Series 19. Linguistics and Intercultural Communication, vol. 27, no. 2, pp. 38-54. (In Russ.) https://doi.org/10.55959/MSU-2074-1588-19-26-3-3, https://elibrary.ru/ivcgto

2. Sorokin D.O. (2025). The use of Character.AI web application for the development of learners’ foreign language communication skills. Inostrannye yazyki v shkole = Foreign Languages at School, no. 2, pp. 59-65. (In Russ.) https://elibrary.ru/kpckof

3. Sysoyev P.V., Ivchenko M.I. (2025). Development of learners’ foreign language pronunciation skills on the basis of artificial intelligence tools. Perspektivy nauki i obrazovaniya = Perspectives of Science and Education, no. 2, pp. 600-614. (In Russ.) https://doi.org/10.32744/pse.2025.2.38, https://elibrary.ru/jrddjj

4. Ivchenko M.I., Polyakov O.G. (2025). Using the ELSA Speak AI tool in pronunciation teaching and learning. Inostrannye yazyki v shkole = Foreign Languages at School, no. 2, pp. 54-58. (In Russ.) https://elibrary.ru/zrvafq

5. Korenev A.A. (2024). Strategies of using artificial intelligence for written corrective feedback in language education. Vestnik Moskovskogo universiteta. Seriya 19. Lingvistika i mezhkul’turnaya kommunikatsiya = Moscow State University Bulletin. Series 19. Linguistics and Intercultural Communication, vol. 27, no. 2, pp. 68-77. (In Russ.) https://doi.org/10.55959/MSU-2074-1588-19-27-2-5, https://elibrary.ru/hizddu

6. Voevoda E.V., Shpynova A.I. (2023). Application of artificial intelligence technologies in learning business English (the case of written assignments). Mir nauki, kul’tury i obrazovaniya, no. 5 (102), pp. 237-240. (In Russ.) https://doi.org/10.24412/1991-5497-2023-5102-237-240, https://elibrary.ru/zepwjz

7. Tormyshova T.Yu., Ryazantseva T.Yu., Sukhanova N.I. (2024). Teaching students of linguistics to write essays in a foreign language based on working with the Criterion automated assessment system. Vestnik Tambovskogo universiteta. Seriya: Gumanitarnye nauki = Tambov University Review. Series: Humanities, vol. 29, no. 1, pp. 99-108. (In Russ.) https://doi.org/10.20310/1810-0201-2024-29-1-99-108, https://elibrary.ru/pdgnzr

8. Evstigneev M.N. (2023). Twee neural network as a new tool for English language teacher. Vestnik Tambovskogo universiteta. Seriya: Gumanitarnye nauki = Tambov University Review. Series: Humanities, vol. 28, no. 6, pp. 1428-1442. (In Russ.) https://doi.org/10.20310/1810-0201-2023-28-6-1428-1442, https://elibrary.ru/hwajhp

9. Almusharraf N., Alotaibi H. (2023). An error-analysis study from an EFL writing context: human and automated essay scoring approaches. Technology, Knowledge and Learning, vol. 28, pp. 1015-1031. https://doi.org/10.1007/s10758-022-09592-z, https://elibrary.ru/nmoqga

10. Dembsey J.M. (2017). Closing the Grammarly® gaps: a study of claims and feedback from an online grammar program. The Writing Center Journal, vol. 36, no. 1, pp. 63-100.

11. Ghufron M.A., Rosyida F. (2018). The role of Grammarly in assessing English as a foreign language (EFL) writing. Lingua Cultura, vol. 12, no. 4, pp. 395-403. https://doi.org/10.21512/lc.v12i4.4582

12. Park J. (2019). An AI-based English grammar checker vs. human raters in evaluating EFL learners’ writing. Multimedia-Assisted Language Learning, vol. 22, no. 1, pp. 112-131. https://doi.org/10.15702/mall.2019.22.1.112

13. Ikegwu A.C., Nweke H.F. & Anikwe C.V. (2023). Recent trends in computational intelligence for educational big data analysis. Iran Journal of Computer Science, vol. 7 (1), pp. 103-129.

14. Khan M.A., Rehman A., Shah A.A., Abbas S., Alharbi M., Ahmad M., Ghazal T.M. (2026). Navigating the future of higher education in Saudi Arabia: implementing AI, machine learning, and big data for sustainable university development. Discover Sustainability, vol. 6 (1), article. 495.

15. Cao F., Lei M., Lin S., Xiang M. (2022). Application of artificial intelligence-based big data AI technology in physical education reform. Mobile Information Systems, vol. 1, article 4017151. https://doi.org/10.1155/2022/4017151, https://elibrary.ru/gdnerm

16. Sysoyev P.V., Filatov E.M., Sorokin D.O. (2024). Feedback in foreign language teaching: from information technologies to artificial intelligence. Yazyk i kul’tura = Language and Culture, no. 65, pp. 242-261. (In Russ.) https://doi.org/10.17223/19996195/65/11, https://elibrary.ru/plzyov

17. Sysoyev P.V., Filatov E.M. (2024). Method of teaching students’ foreign language creative writing based on evaluative feedback from artificial intelligence. Perspektivy nauki i obrazovaniya = Perspectives of Science and Education, no. 1 (67), pp. 115-135. (In Russ.) https://doi.org/10.32744/pse.2024.1.6, https://elibrary.ru/tmstly

18. Filatov E.M. (2026). Development of writing skills based on evaluation and corrective feedback from artificial intelligence. Inostrannye yazyki v shkole = Foreign Languages at School, no. 2, pp. 42-52. https://elibrary.ru/jmkenc (In Russ.)