BRIDGING THE LANGUAGE BARRIER: AI-DRIVEN ADAPTIVE LEARNING FOR MEDICAL STUDENTS
Keywords:
AI-driven learning, medical terminology, EMP, natural language processing, non-English speaking environment, personalized education.Abstract
This study examines the development and implementation of an AI-driven adaptive learning system designed to enhance medical terminology acquisition among students in non-English speaking academic settings. By integrating machine learning and natural language processing (NLP) into English for Medical Purposes (EMP) curricula, the research evaluates the system's impact on vocabulary retention, communicative competence, and personalized educational trajectories.
References
1. Buranova D.D. “To teach or not to teach English for medical students” Psychology and education(2021) 58(3), p.907
2. Luckin, R., & Holmes, W. (2016). Intelligence Unleashed: An argument for AI in Education. Pearson. (Классика по ИИ в образовании).
3. Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16-24. doi.org
4. Zhang, K., & Aslan, A. B. (2021). AI technologies for personalized learning and adaptive assessment: A systematic review. International Journal of Educational Technology in Higher Education, 18(1), 1-25.
5. Hull, M. (2016). Medical English teaching and learning. Medical Education, 50(10), 1032-1039.
6. Klimova, B. (2019). Impact of mobile learning on students' achievement results. Education Sciences, 9(2), 90. (Обучение в неанглоязычной среде).
7. Antoniou, P. P., & Fragidis, L. L. (2022). Adaptive Learning in Medical Education: A Review of Strategies and Systems. Journal of Medical Systems, 46(11), 78.
8. Greta Franzini, Marco Passarotti, Francesco Mambrini, Giovanni Moretti, Federica Zampedri, Græcissāre: Ancient Greek Loanwords in the LiLa Knowledge Base of Linguistic Resources for Latin, Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020, 10.4000/books.aaccademia.8565, (199-204), (2021).
9. Michèle Fruyt Word-Formation in Classical Latin.Ed. James Clackson; 2011 https://doi.org/10.1002/9781444343397.ch11
10. Nabieva J.R., Guzacheva N.I., Zakhidova M.F. Teaching listening as one of the beneficial Approaches in learning languages; Science and innovation international scientific journal volume 2 issue 10 october 2023 uif-2022: 8.2 | issn: 2181-3337
11. Sharipova F.I. (2023) Teaching undergraduate students clinical vocabulary. Scientific journal of the Fergana State University. Fergana. №2, pp.149-153
12. Peters, E. (2020). The effect of word properties on intentional vocabulary learning: The case of medical terminology. System, 94, 102319.
13. Zou, D., Huang, Y., & Xie, H. (2021). Natural language processing methods for vocabulary learning: A review. Computer Assisted Language Learning, 34(5-6), 612-638.
14. Peters, E. (2020). The effect of word properties on intentional vocabulary learning: The case of medical terminology. System, 94, 102319.
15. Zou, D., Huang, Y., & Xie, H. (2021). Natural language processing methods for vocabulary learning: A review. Computer Assisted Language Learning, 34(5-6), 612-638.
