Ding, H. orcid.org/0000-0002-5747-7796 and Homer, M. orcid.org/0000-0002-1161-5938 (2026) Exploring student and faculty experiences and perceptions of generative artificial intelligence in medical education. Discover Education, 5 (1). 832. ISSN: 2731-5525
Abstract
The potential and challenges of using Generative artificial intelligence (GAI) in medical education are widely discussed, yet its use by medical students and faculty remains under-researched in the UK context. This exploratory sequential mixed-methods pilot study empirically investigates and compares student and faculty experiences and perceptions of using GAI in medical education. A questionnaire was developed based on focus groups and administered to undergraduate medical students and faculty within a UK medical school. Descriptive analysis and comparative analysis were used to quantify and compare student (n = 29) and faculty (n = 32) experiences and perceptions of GAI. Reflexive thematic analysis of open-ended question responses was undertaken to complement quantitative results. We found that GAI is being used for a variety of purposes by students and faculty in learning/teaching/assessment. However, both of them generally have relatively low self-confidence in GAI-related knowledge and skills. Faculty members tend to have stronger concerns of GAI limitations and its ethical challenges. Males are more confident in using GAI and have more positive attitudes towards GAI than females. The findings also provide preliminary evidence for the importance of open communication of GAI between students and faculty and the need for schools’, universities’, and national regulators’ strategic support to ensure faculty and student ethical and effective use of GAI and equitable access to GAI technology between subgroups. Our findings and the pilot tools can inform future research investigating the use of GAI in medical education in other institutional context or at a larger scale.
Metadata
| Item Type: | Article |
|---|---|
| Authors/Creators: |
|
| Copyright, Publisher and Additional Information: | © The Author(s) 2026. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licens es/by-nc-nd/4.0/. |
| Keywords: | Medical education; Generative artificial intelligence; Undergraduate students; Faculty |
| Dates: |
|
| Institution: | The University of Leeds |
| Academic Units: | The University of Leeds > Faculty of Education, Social Sciences and Law (Leeds) > School of Education (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Medical Education |
| Funding Information: | Funder Grant number ASME (Association for the Study of NO EXT REF |
| Date Deposited: | 18 Sep 2026 14:12 |
| Last Modified: | 18 Sep 2026 14:12 |
| Status: | Published |
| Publisher: | Soringer |
| Identification Number: | 10.1007/s44217-026-02106-4 |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:244990 |
Download
Filename: s44217-026-02106-4.pdf
Licence: CC-BY-NC-ND 4.0

CORE (COnnecting REpositories)
CORE (COnnecting REpositories)