UDC 378.147:004.8:811

Nikolaeva N.N.¹ (Moscow, Russian Federation) – nnn55n73@mail.ru; Lezhneva E.B.¹ (Moscow, Russian Federation) – ekatlezhneva@mail.ru

¹Bauman Moscow State Technical University

Abstract. Thepaper considers the application of generative artificial intelligence (AI) to the English learning at a technical university, particularly at Bauman Moscow State Technical University. This research is relevant because AI raises fundamentally new issues concerning foreign language learning motivation in an “intellectually enhanced” environment. The aim is to identify and categorize some motivational and demotivational factors that influence technical university students’ learning English as well as to suggest ways for mitigating negative educational outcomes of AI usage. The study is based on E. Deci and R. Ryan’s self-determination theory, L.S. Vygotsky’s socio-cultural theory and cognitive load theories. Our psychometric toolkit consists of an adapted scale for measuring basic psychological needs in an AI-mediated environment, a scale for academic demotivation, and a semi-structured interview. Correlation, regression, and clustering methods are employed to analyze quantitative data. A thematic content analysis and discourse analysis are used for data interpretation. The results reveal that the AI impact on learning motivation is ambivalent and stage-mediated ranging from instrumental motivation to academic demotivation and “engaged amotivation.” The novelty lies in identifying four clusters of motivational responses to AI assistants, which differ in terms of autonomy, competence, and connection to the group. The paper proves a crucial role of a teacher’s facilitative support as a resource for students’ educational self-regulation. Our findings contribute to the psychological theory of AI-mediated foreign language learning and suggest a practical typology of motivational risks and benefits of AI use in a technical university context.

Keywords: English for specific purposes, technical university, artificial intelligence, motivation, demotivation, theory of self-determination, cognitive load, digital competence.

References:  

1. Bocharova, M. N. (2024). Formation of a new linguistic personality in teaching a foreign language in a non-linguistic university in the context of digitalization: change of motivational triggers. Philological Sciences. Issues of Theory and Practice, 17(3), 635–640. (In Russian). https://doi.org/10.30853/phil20240091  
2. Veduta, O. V. (2020). Formation of motivation of technical university students to learn a foreign language. Professional Education in Russia and Abroad, 1(37), 84–91. (In Russian).  https://www.elibrary.ru/afrwvw
3. Vygotsky, L. S. (2014). Thinking and Speech. Moscow: Directmedia.
4. Grineva, M. V., & Bunina, T. A. (2026). Maintaining motivation to learn a foreign language for professional purposes at university in the context of availability of electronic translation services. Practical and pedagogical aspect. Human Capital, 2(206), 49–62. (In Russian).  https://doi.org/10.25629/HC.2026.02.03
5. Ilyushchenko, N. S., Shifrina, S. A., & Tsoloeva, S. B. (2025). Motivation and ways of using. Modern Science: Actual Problems of Theory and Practice. Series: Humanities, 4/2, 86–91. (In Russian).  https://doi.org/10.37882/2223-2982.2025.04-2.16
6. Pinchuk, A. N., Tikhomirov, D. A., & Karepova, S. G. (2023). Academic motivation of modern students: cluster analysis experience. Social and Humanitarian Knowledge, 9(2), 210–219. (In Russian). https://doi.org/10.18255/2412-6519-2023-2-210-219  
7. Polyudova, E. N., & Bykova, E. A. (2025). Artificial intelligence as a tool for adapting authentic materials in teaching English at a technical university.  Concept, 8, 102-124. (In Russian).  https://doi.org/10.24412/2304-120X-2025-11154  
8. Cao, L., & Abdullah, A. (2025). EBA (Engaged but Amotivated) in AI-enhanced EFL learning: a qualitative study from a Chinese higher vocational context. Frontiers in Psychology, 16, 1643653. https://doi.org/10.3389/fpsyg.2025.1643653  
9. Dang, G. N., Van Vu, T., & La, M. N. B. (2023). Investigating demotivating factors in learning English for specific purposes at a higher education institution. Russian Psychological Journal, 20(1), 162–181. https://doi.org/10.21702/rpj.2023.1.11  
10. Deci, E. L., & Ryan, R. M. (2013). Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media. https://doi.org/10.1016/B978-0-12-809324-5.05613-3
11. Dörnyei, Z. (2009). The L2 Motivational Self System. In Z. Dörnyei & E. Ushioda (Eds.), Motivation, Language Identity and the L2 Self (pp. 9–42). Multilingual Matters. https://doi.org/10.1016/j.system.2009.08.002  
12. Ebadi, S., & Amini, A. (2024). Examining the roles of social presence and human-likeness on Iranian EFL learners’ motivation using artificial intelligence technology: A case of CSIEC chatbot. Interactive Learning Environments, 32, 655–673. https://doi.org/10.1080/10494820.2022.2096638   
13. Gunasekara, S., & Kumarage, P. (2026). A meta-analysis of generative AI effects on language proficiency and affective–cognitive outcomes in language learning. Discover Computing, 29, Article 116. https://doi.org/10.1007/s10791-026-10015-1  
14. Hao, H. (2026). AI-driven analysis of English learners’ perception, attitude, and motivation in online learning environments: a corpus-based study. Interactive Learning Environments, 1, 1–15. https://doi.org/10.1080/10494820.2026.2649557  
15. Liu, F. (2026). AI literacy and foreign language boredom in AI-assisted academic English learning: the roles of engagement and task value. Frontiers in Psychology, 17, 1795301. https://doi.org/10.3389/fpsyg.2026.1795301  
16. McDonald, N., Johri, A., Ali, A., & Collier, A. H. (2025). Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines. Computers in Human Behavior: Artificial Humans, 3, 100121. https://doi.org/10.1016/j.chbah.2025.100121  
17. Moybeka, A. M. S., Syariatin, N., Tatipang, D. P., Mushthoza, D. A., Dewi, N. P. J. L., & Tineh, S. (2023). Artificial Intelligence and English Classroom: The Implications of AI toward EFL Students’ Motivation. Edumaspul: Jurnal Pendidikan, 7, 2444–2454. https://doi.org/10.33487/edumaspul.v7i2.6669  
18. Song, C., & Song, Y. (2023). Enhancing academic writing skills and motivation: assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students. Frontiers in Psychology, 14, 1260843. https://doi.org/10.3389/fpsyg.2023.1260843  
19. Sumakul, D. T. Y. G., & Hamied, F. A. (2023). Amotivation in AI injected EFL classrooms: Implications for teachers. Indonesian Journal of Applied Linguistics, 13(1), 26–34. https://doi.org/10.17509/ijal.v13i1.58254   
20. Sweller, J. (2011). Cognitive load theory. In J. P. Mestre & B. H. Ross (Eds.), The psychology of learning and motivation (Vol. 55, pp. 37–76). Academic Press. https://doi.org/10.1016/B978-0-12-387691-1.00002-826  
21. Tataurova, V. (2024). Teaching English at Tertiary Level: Motivating Students of Technical Study Programs. International Journal of Higher Education Pedagogies, 5(2), 41–55. https://doi.org/10.33422/ijhep.v5i2.634  
22. Teng, M. F. (2025). Examining longitudinal development of writing motivation in the GenAI context: A self-determination theory perspective. Learning and Motivation, 91, 102157. https://doi.org/10.1016/j.lmot.2025.102157  
23. Yashchuk, O. (2024). ESP as an Approach for Implementing University Engineering Students’ Language Skills through the Prism of Their Needs and Motivation. Baltic Journal of Legal and Social Sciences, 3, 135–143. https://doi.org/10.30525/2592-8813-2024-3-13  
24. Zheng, Y., Wang, Y., Liu, K. S. X., et al. (2024). Examining the moderating effect of motivation on technology acceptance of generative AI for English as a foreign language learning. Education Information Technology, 29, 23547–23575. https://doi.org/10.1007/s10639-024-12763-3

For citation: Nikolaeva N.N., Lezhneva E.B. (2026). Artificial intelligence impact on (de)motivation of technical university students’ learning English. CITISE, 2, 1029-1042. (In Russian).