Integrating Artificial Intelligence into Adaptive ESP Curriculum Design: Pedagogical Innovations for Specialized Language Learning

Eslam Shehata Ahmed Yacoub

doi:10.18860/jeasp.v9i1.38531

Integrating Artificial Intelligence into Adaptive ESP Curriculum Design: Pedagogical Innovations for Specialized Language Learning

Eslam Shehata Ahmed Yacoub

Abstract

The rapid expansion of artificial intelligence (AI) in education has renewed scholarly interest in the ways technology can enhance English for Specific Purposes (ESP) curriculum design, particularly in highly specialized fields such as healthcare. The present study investigates the pedagogical impact of integrating AI-driven feedback into an adaptive ESP writing curriculum and examines its effects on learners' acquisition of domain-specific vocabulary and communicative competence. A convergent mixed-methods design was employed: undergraduate healthcare students enrolled in a medical English ESP course were assigned to either an AI-enhanced instructional condition or a traditional instructional condition. Quantitative analyses of pre-test and post-test writing tasks revealed significantly greater gains for the AI-enhanced group in grammatical accuracy, lexical specificity, and overall textual coherence, with these gains corroborated by platform analytics from the Write & Improve system. Qualitative evidence drawn from semi-structured student interviews and faculty observation notes demonstrated strong learner engagement, positive perceptions of the immediacy of AI-generated feedback, and increased confidence in revising specialized texts, alongside concerns regarding the potential for over-reliance on automated feedback. The findings indicate that AI-supported personalization can meaningfully enhance ESP learning outcomes when it is integrated within a pedagogically coherent curriculum. The study contributes empirical evidence to the emerging field of AI-informed ESP research and offers practical implications for the design of adaptive, technology-supported ESP instruction in both professional and academic settings.

Full Text:

PDF

References

Abrorov, A., Saidov, A., Meyliyeva, N., Abdullaeva, D., & Shukurova, M. (2025). Optimizing mechanical engineering curriculum design using AI and machine learning. AIP Conference Proceedings, 3268(1). https://doi.org/10.1063/5.0258030

Assassi, T., & Rouaghe, F. (2025). Exploring Subject Knowledge Gap in ESP Teaching: Perspectives of Language and Subject Specialist Teachers on AI Integration. Teaching English Language, 19(1), 185–225. https://doi.org/10.22132/tel.2024.460057.1621

Baker, G., & Caton, L. (2025). AI-Powered Pedagogy and Curriculum Design: Practical Insights for Educators. https://doi.org/10.4324/9781003544920

Bondi, M. (2025). AI and writing: Challenges and opportunities for ESP and professional communication. International Journal of Language Studies, 19(2), 51–74. https://doi.org/10.5281/zenodo.15250813

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Catenaccio, P. (2025). AI and discourse analysis: Implications for ESP genre pedagogy in EFL settings. International Journal of Language Studies, 19(2), 31–50. https://doi.org/10.5281/zenodo.15250799

Conrad, E. J., & Hall, K. C. (2024). Leveraging Generative AI to Elevate Curriculum Design and Pedagogy in Public Health and Health Promotion. Pedagogy in Health Promotion, 10(3), 178–186. https://doi.org/10.1177/23733799241232641

Dahake, P. S., Santo, V. R., & Panchariya, R. (2025). AI-powered knowledge management in curriculum design and content delivery (pp. 415–453). https://doi.org/10.4018/979-8-3373-5092-9.ch015

Dou, W. (2024). The Application of Generative AI Technology in ESP Courses for Civil Engineering Majors. 144–147. https://doi.org/10.1109/ICAIDT62617.2024.00039

Elmuratova, A., Sabirova, G., Alautdinova, K., Abdullaeva, M., Uteshova, Z., Elmuratova, S., Arzieva, B., & Kannazarova, Z. (2025). Increased Popularity of AI and Digital Technologies in ESP: A Bibliometric Review. Forum for Linguistic Studies, 7(6), 602–616. https://doi.org/10.30564/fls.v7i6.9715

Flechtner, R., & Stankowski, A. (2023). AI Is Not a Wildcard: Challenges for Integrating AI into the Design Curriculum. 72–77. https://doi.org/10.1145/3587399.3587410

Fleischmann, K. (2024). Making the case for introducing generative artificial intelligence (AI) into design curricula. Art, Design and Communication in Higher Education, 23(2), 187–207. https://doi.org/10.1386/adch_00088_1

Guo, M., Xie, Y., Han, L., Lei, V. L. C., & Li, D. (2025). Bridging traditional and AI-assisted simultaneous interpreting: empirical insights for curriculum design. Interpreter and Translator Trainer, 19(3–4), 425–443. https://doi.org/10.1080/1750399X.2025.2533007

Haryani, S., & Emha, M. R. (2025). Designing AI-integrated e-book for English for specific purposes (ESP) in health science education. 8(2), 118–132. https://doi.org/10.12928/eltej.v8i2.14229

Kim, K., & Kwon, K. (2025). From co-design to co-teaching: a comprehensive approach to an integrated AI curriculum in middle school STEM education. Smart Learning Environments, 12(1). https://doi.org/10.1186/s40561-025-00413-1

Komang Mertayasa, I., & Saputra, P. W. (2025). Innovating Curriculum Design with AI and Gamification: A Roadmap for Society 5.0 (pp. 295–324). https://doi.org/10.4018/979-8-3373-1062-6.ch010

Li, F. (2025). AI-Enhanced Curriculum Design and Deep-Learning-Based Assessment in International Sports Communication Education. International Journal of Information and Communication Technology Education, 21(1). https://doi.org/10.4018/IJICTE.391357

Lin, P., & Brummelen, J. V. (2021). Engaging teachers to co-design integrated ai curriculum for k-12 classrooms. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3411764.3445377

Liu, J., Ma, J., & Li, S. (2025). Research on school-based AI curriculum design and practice for cultivating computational thinking in high school students. Education and Information Technologies, 30(6), 7949–7993. https://doi.org/10.1007/s10639-024-13115-x

Long, T. T., Lam, N. T., & Cuong, T. Q. (2025). AI Integration into Curriculum Design and Development: Theoretical Framework and Application. Lecture Notes in Networks and Systems, 1399 LNNS, 352–361. https://doi.org/10.1007/978-3-031-91005-0_32

Pan, M., Wu, B., & Zhang, Y. (2020). Curriculum Design of AI Experimental Course Based on CDIO Model. Advances in Intelligent Systems and Computing, 1075, 525–531. https://doi.org/10.1007/978-3-030-32591-6_56

Rutecka, P., Cicha, K., Rizun, M., & Strzelecki, A. (2025). Generative AI in Curriculum Design: Empirical Insights Into Model Performance and Educational Constraints. IEEE Transactions on Learning Technologies, 18, 757–768. https://doi.org/10.1109/TLT.2025.3587081

Salhab, R. (2024). AI Literacy Across Curriculum Design: Investigating College Instructors’ Perspectives. Online Learning Journal, 28(2). https://doi.org/10.24059/olj.v28i2.4426

Selvakumar, P., Tamizharasu, S., Singh, K., & Lakshmi Prasanna, L. (2025). Curricula and instructional design for AI (pp. 17–35). https://doi.org/10.4018/979-8-3693-8965-2.ch002

Su, J., & Zhong, Y. (2022). Artificial Intelligence (AI) in early childhood education: Curriculum design and future directions. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100072

Taylor, P. (2024). Blended Learning Challenges of EFL Undergraduate Students: Student Learning Experience in an AI-integrated ESP Course. Studies in English Language and Education, 11(3), 1231–1449. https://doi.org/10.24815/siele.v11i3.37472

Usman, N., Kusumanegara, A., Husnaini, H., Said, Y. R., & Ismayanti, D. (2025). Cultural Bias and Linguistic Representation in AI Curriculum Design: Implications for Society 5.0 (pp. 229–258). https://doi.org/10.4018/979-8-3373-1062-6.ch008

Yin, D., Li, Q., Karimi, A., Mitsakos, N., El Mahbes, R., & Cox, J. (2024). Integration of AI and Domain Expert Knowledge to Improve ESP Efficiency and Performance. https://doi.org/10.2118/221565-MS

DOI: https://doi.org/10.18860/jeasp.v9i1.38531