AI-Powered Personalization in ESP: Enhancing Learner Autonomy and Engagement in English for Professional Contexts

The educational landscape has been greatly transformed by recent advances in Artificial Intelligence (AI) to facilitate highly personalized and adaptive learning environments. Personalization is particularly important in English for Specific Purposes (ESP) where teaching focuses on the linguistic and communicational needs of professionals in specific domains. Yet, despite the large-scale integration of AI tools into wider education, there is a significant gap in conceptual literature on how AI can best be employed to boost learner autonomy and engagement in ESP contexts. This conceptual paper investigates the transformative role of Artificial Intelligence (AI) in enhancing personalization, learner autonomy, and engagement within English for Specific Purposes (ESP) instruction. While AI tools such as intelligent tutoring systems, chatbots, adaptive learning platforms, and learning analytics have increasingly permeated general language education, their pedagogical integration into the specialized contexts of ESP remains under-theorized. To address this gap, the study synthesizes findings from 30 peer-reviewed journal articles published between 2015 and 2025, employing a thematic literature review approach to derive a comprehensive, interdisciplinary framework for AI-enhanced ESP learning. Five main themes emerged from the synthesis: (1) personalized learning paths through adaptive technologies, (2) real-time, AI-powered feedback, (3) chatbot-facilitated learner autonomy, (4) gamification-supported engagement, and (5) ethical and pedagogical considerations in AI integration. These dimensions were examined across diverse ESP domains including engineering, business, academic writing, healthcare, and tourism revealing how AI-driven instruction can address domain-specific linguistic and professional communication needs. The proposed framework emphasizes the central role of personalization in supporting autonomous, engaging learning experiences, while also underscoring the need for ethical, context-aware design and responsible instructional alignment. This paper contributes a structured conceptual model that bridges applied linguistics, educational technology, and AI studies, offering both theoretical insight and practical guidance. Thus, it highlights the need for human-AI complementary, ethical for AI supplemented ESP pedagogy are discussed including the transparency and domain compliance. It underscores the significance of human-AI collaboration, ethical transparency, and domain alignment in the implementation of AI-enhanced ESP pedagogy. This study underscores the necessity for ethical, human-centered AI integration in ESP instruction and advocates for ongoing empirical research to substantiate the proposed framework, thereby guaranteeing scalable, inclusive, and pedagogically robust applications of AI in global language learning environments

Artificial Intelligence (AI) in Education, English for Specific Purposes (ESP); Learner Autonomy

1. Ahmed, F. (2024). The digital divide and AI in education: Addressing equity and accessibility. AI EDIFY Journal, 1(2), 12-23. [Google Scholar] [Crossref]

2. Ali, R., AbdAlgane, M., Youssif, E., Elkot, M. A., & Abbass, D. (2024). Elevating English reading comprehension: The synergy of dialogic teaching and technology integration in ESP learning environments. International Journal of Information and Education Technology, 14(8), 1109-1118. [Google Scholar] [Crossref]

3. Deng, X., & Yu, Z. (2022). A systematic review of machine-translation-assisted language learning for sustainable education. Sustainability, 14(13), 7598. [Google Scholar] [Crossref]

4. Dewantara, K. A. K., Laksana, I. P. Y., & Saraswati, N. P. R. T. A. K. H. (2024). A Systematic Review of AI-Powered Tools in English Language Learning for Vocational Students. International Journal of Natural Science and Engineering, 8(3), 201–214. https://doi.org/10.23887/ijnse.v9i2.101889 [Google Scholar] [Crossref]

5. Du, Q. (2025). How artificially intelligent conversational agents influence EFL learners' self-regulated learning and retention. Education and Information Technologies, 1-67. [Google Scholar] [Crossref]

6. Eshbayev, O. A., Maxmudov, A. X., & Rozikov, R. U. (2022). An overview of a state of the art on developing soft computing-based language education and research systems: A survey of engineering English students in Uzbekistan. In Proceedings of the 5th International Conference on Future Networks and Distributed Systems (ICFNDS '21) (pp. 447–452). Association for Computing Machinery. https://doi.org/10.1145/3508072.3508160 [Google Scholar] [Crossref]

7. Hsu, K. C., Barrett, N. E., & Liu, G. Z. (2025). English for tourism and AR-assisted context-aware ubiquitous learning: a preliminary design-based research study. Computer Assisted Language Learning, 38(3), 544-568. [Google Scholar] [Crossref]

8. Kalimbetova, X. (2025). ROLES OF AI PROGRAMS IN TEACHING ENGLISH FOR ESP STUDENTS. Наука и инновация, 3(24), 196-199. [Google Scholar] [Crossref]

9. Khan, I. A., Khan, K. A., & Mahmood, N. (2025). The Future of English Language Teaching in Pakistan: A Comparative Analysis of AI-Driven and Human-Led Instruction. Liberal Journal of Language & Literature Review, 3(1), 315-330. [Google Scholar] [Crossref]

10. Khellab, F., Demirel, Ö., & Mohammadzadeh, B. (2022). Effect of Teaching Metacognitive Reading Strategies on Reading Comprehension of Engineering Students. SAGE Open, 12(4). https://doi.org/10.1177/21582440221138069 [Google Scholar] [Crossref]

11. Khreisat, M.N. (2022). English Language Learning Strategies during COVID-19 in the Middle East: A Systematic Review. Arab World English Journal, 13 (1) 56-71. [Google Scholar] [Crossref]

12. https://dx.doi.org/10.24093/awej/vol13no1.4 [Google Scholar] [Crossref]

13. Liashenko, I., Chepeliuk, N., & Rumiantseva, O. (2025). ENHANCING ESP FOR STEM STUDENTS: AI TOOLS AND THEIR EFFECTS ON LANGUAGE AND CONTENT LEARNING. Philological Treatises, 17(1), 284–293. https://doi.org/10.21272/Ftrk.2025.17(1)-25 [Google Scholar] [Crossref]

14. Lin, C. J., & Hwang, G. J. (2025). Artificial intelligence-supported procedural scaffolding for promoting EFL learners’ writing performance in flipped peer assessment activities. Interactive Learning Environments, 1–15. https://doi.org/10.1080/10494820.2025.2532629 [Google Scholar] [Crossref]

15. Le Quang, D. (2021). From generalization to personalization: Designing a personalized ESP teaching model in a Vietnamese private university context. Social Science and Humanities Journal, 9(4), 7498–7510. https://doi.org/10.18535/sshj.v9i04.1784 [Google Scholar] [Crossref]

16. Luckin, R., & Holmes, W. (2016). Intelligence unleashed: An argument for AI in education. UCL Knowledge Lab. https://discovery.ucl.ac.uk/id/eprint/1475756/ [Google Scholar] [Crossref]

17. Nazari, O., & Atai, M. R. (2022). An exploratory study of EAP teachers' coping strategies. System, 106, 102764. https://doi.org/10.1016/j.system.2022.102764 [Google Scholar] [Crossref]

18. Ng, S. L., & Ho, C. C. (2025). Generative AI in Education: Mapping the Research Landscape Through Bibliometric Analysis. Information, 16(8), 657. [Google Scholar] [Crossref]

19. Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. P. T. (2023). Ethical principles for artificial intelligence in education. Education and information technologies, 28(4), 4221-4241. [Google Scholar] [Crossref]

20. Nguyen, H. A. (2024). Harnessing AI-based tools for enhancing English speaking proficiency: Impacts, challenges, and long-term engagement. International Journal of AI in Language Education, 1(2), 18-29. [Google Scholar] [Crossref]

21. Pérez, C., & González, M. (2023). Ethical implications of AI in ESP: Design and fairness. Journal of Educational Innovation and Technology, 10(2), 112–129. [Google Scholar] [Crossref]

22. Pitirimova, T. V. (2025). Artificial intelligence in foreign language learning for specific purposes: The use of AI chatbots, virtual assistants, and adaptive programs. In N. A. Gunina (Ed.), Artificial intelligence in foreign language learning (pp. 158–167). Tambov State Technical University. https://tstu.ru/book/elib1/pdf/2025/Gunina2.pdf#page=158 [Google Scholar] [Crossref]

23. Rudneva, M. (2025). AI for pedagogic design in ESP. In EDULEARN25 Proceedings of the 17th International Conference on Education and New Learning Technologies (pp. 4758–4766). IATED. https://doi.org/10.21125/edulearn.2025.1215 [Google Scholar] [Crossref]

24. Salem, A. A. M. S. (2025). Harnessing Wiki-Mediated Collaborative Writing to Enhance ESP Students’ Persuasive Writing, 21st-Century Skills, and Self-Efficacy. Journal of Educational Computing Research, 07356331251337500. [Google Scholar] [Crossref]

25. Stuart J. Barnes. (2021). Understanding terror states of online users in the context of COVID-19: An application of Terror Management Theory. Computers in Human Behavior, 125, 106967. https://doi.org/10.1016/j.chb.2021.106967 [Google Scholar] [Crossref]

26. Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025). AI‐driven adaptive learning for sustainable educational transformation. Sustainable Development, 33(2), 1921-1947. [Google Scholar] [Crossref]

27. Unsworth, L., & Mills, K. A. (2020). English language teaching of attitude and emotion in digital multimodal composition. Journal of Second Language Writing, 47, 100712. https://doi.org/10.1016/j.jslw.2020.100712 [Google Scholar] [Crossref]

28. Wiboolyasarin, W., Wiboolyasarin, K., Tiranant, P., Jinowat, N., & Boonyakitanont, P. (2025). AI-driven chatbots in second language education: A systematic review of their efficacy and pedagogical implications. Ampersand, 14, 100224. [Google Scholar] [Crossref]

29. Hashim, S., Omar, M. K., Ab Jalil, H., & Sharef, N. M. (2022). Trends on technologies and artificial intelligence in education for personalized learning: systematic literature. Journal of Academic Research in Progressive Education and Development, 12(1), 884-903. [Google Scholar] [Crossref]

30. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning (1st ed.). Center for Curriculum Redesign. https://discovery.ucl.ac.uk/id/eprint/10139722/ [Google Scholar] [Crossref]

31. Snyder, H. (2019). Literature reviews as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039 [Google Scholar] [Crossref]

32. Yuping Wang, Xibin Han, & Juan Yang. (2015). Revisiting the Blended Learning Literature: Using a Complex Adaptive Systems Framework. Journal of Educational Technology & Society, 18(2), 380–393. http://www.jstor.org/stable/jeductechsoci.18.2.380 [Google Scholar] [Crossref]

33. Xatamova, N., & Ashurov, J. (2024). The Future of Legal English Learning: Integrating AI into ESP Education. SPAST Reports, 1(7). [Google Scholar] [Crossref]

34. Xayitmuradova, S. M. (2025). THE ROLE OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN ESP TEACHING. Экономика и социум, (3-2 (130)), 307-311. [Google Scholar] [Crossref]

35. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0 [Google Scholar] [Crossref]

36. Zhao, J. Advancing english language education: A mixed-methods analysis of AI-driven tools'impact on engagement, personalization, and learning outcomes. Educ Inf Technol (2025). https://doi.org/10.1007/s10639-025-13560-2 [Google Scholar] [Crossref]

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