Enhancing English Medical Terminology Learning through ChatGPT-Driven Prompts

DOI

10.2991/978-94-6239-634-0_18How to use a DOI?

Keywords

Artificial Intelligence; Medical English; Morocco; Autonomous Learning; ChatGPT; ESP; Higher Education; Language Policy

Abstract

English is the universal language of medical science. In Morocco, however, medical education remains predominantly francophone. The curriculum mandates full immersion in rigorous, discipline-specific medical content presented exclusively in French, creating a significant linguistic disparity with the discipline’s English-dominant global literature and research. With Medical English restricted to just two hours per week for only one semester annually, independent, autonomous learning is an educational necessity. This paper evaluates Generative AI (GenAI) as a mechanism to support such autonomy. This study involved two parallel groups of first-year students at the Faculty of Medicine and Pharmacy in Tangier. The control group utilized the Community Language Learning method, while the intervention group supplemented this approach with independent practice using structured ChatGPT prompts. Post-intervention analysis revealed that the intervention group achieved a statistically higher mean score on the validated English Medical Terminology assessment. Furthermore, qualitative data from student interviews and written reflections indicated that students perceived the GenAI tool as a non-judgmental and low-anxiety space for practicing terminology. The findings suggest that AI is an effective, readily accessible tool that can compensate for limitations in scheduled classroom hours. Structured prompt-based learning environments yield significant improvements in student engagement, retention, and confidence. The paper concludes by proposing an integrative model of hybrid instruction for Moroccan higher education that combines traditional pedagogy with AI-mediated autonomy to bridge linguistic and epistemic divides in medical education.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Omar Ajbar
PY  - 2026
DA  - 2026/04/02
TI  - Enhancing English Medical Terminology Learning through ChatGPT-Driven Prompts
BT  - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2025)
PB  - Atlantis Press
SP  - 216
EP  - 232
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6239-634-0_18
DO  - 10.2991/978-94-6239-634-0_18
ID  - Ajbar2026
ER  -