Student and teacher views of using GenAI in GP undergraduate medical education: 'to complement not replace'.

Authors

Millie Ramsay, Hugh Alberti

Published in

Education for primary care : an official publication of the Association of Course Organisers, National Association of GP Tutors, World Organisation of Family Doctors. Pages 1-4. Aug 11, 2026. Epub Aug 11, 2026.

Abstract

The use of Generative Artificial intelligence (GenAI) in both health care and medical education has generated great excitement and a plethora of research. However, little work has been done in the area of undergraduate general practice education. We aimed therefore to explore the experiences and views of students and teachers of GenAI use in medical education. This was a multi-method UK study involving a national survey of GP heads of teaching, focus groups of medical students and interviews with GP teachers and heads of GP teaching. Interviews and focus groups were transcribed verbatim and thematically analysed. Only 5 of 19 GP heads of teaching (40% response rate) who responded report formal use of GenAI, with 11 of 19 reporting informal use within their institutions. Three overarching themes were constructed from the qualitative results. Firstly, positivity towards GenAI use: teachers and students demonstrated enthusiasm for generative AI, with improved efficiency and time-saving consistently identified as the most significant benefits across both groups. Secondly, concerns about GenAI use: these included inaccuracies, de-skilling, data protection and the environmental impact. Thirdly, a shared view that AI should complement rather than replace human expertise. Our study has revealed consensus between GP teachers and students acknowledging AI's unavoidable role in the future of medical education and healthcare, alongside a variety of challenges. A number of current uses are presented specific to the undergraduate GP education context. The underlying consensus emphasised complementing rather than replacing the doctor's role when implementing AI into practice.

PMID:
42579624
Bibliographic data and abstract were imported from PubMed on 12 Aug 2026.

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