Artificial Intelligence in Health Professions Education assessment: AMEE Guide No. 178
Abstract
Health Professions Education (HPE) assessment is being increasingly impacted by Artificial Intelligence (AI), and institutions, educators, and learners are grappling with AI's ever-evolving complexities, dangers, and potential. This AMEE Guide aims to assist all HPE stakeholders by helping them navigate the assessment uncertainty before them. Although the impetus is AI, the Guide grounds its path in pedagogical theory, considers the range of human responses, and then deals with assessment types, challenges, AI roles as tutor and learner, and required competencies. It then discusses the difficult and ethical issues, before ending with considerations for faculty development and the technicalities of AI acknowledgment in assessment. Through this Guide, we aim to allay fears in the face of change and demonstrate possibilities that will allow educators and learners to harness the full potential of AI in HPE assessment.
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AI tools, such as automated grading systems and chatbots for feedback, offer the potential to scale personalized assessments.
“AI tools, such as automated grading systems and chatbots for feedback, offer the potential to scale personalized assessments, but their ethical implications must be carefully considered [140-142].”
From Formative assessment and feedback in medical education: A practical guide: AMEE Guide No. 189 (2026) · cites this paper as [142]
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- Authors
- Masters, K., MacNeil, H., Benjamin, J., Carver, T., Nemethy, K., Valanci-Aroesty, S., Taylor, D. C. M., Thoma, B., & Thesen, T.
- Affiliations
- Sultan Qaboos University (OM)
- Department of Medicine, University of Toronto
- Baylor College of Medicine (US)
- McGill University (CA)
- Baycrest Academy for Research and Education (CA)
- Royal College of Physicians and Surgeons of Canada
- Gulf Medical University (AE)
- Toronto Metropolitan University (CA)
- Dartmouth College (US)
- Corresponding author
- Masters, Ken
- ORCID
- Venue
- Medical Teacher
- Publisher
- Informa UK Limited
- Published
- 2025-01-09
- Volume
- 47
- Issue
- 9
- Pages
- 1410-1424
- ISSN
- 0142-159X (ISSN-L)
- 1466-187X
- OpenAlex
- W4406200047
- Semantic Scholar
- 913e69982ef63e338a9f4d80e39bd09be036cf38
- Cited by
- 80
- References
- 80
- Citations by year
- 2026: 50 · 2025: 30
- FWCI
- 19.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Teacher (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- artificial intelligence, health professions education, assessment, AI tutors, evaluation ethics, faculty development, AI competencies, pedagogical theory
- MeSH terms
- Artificial Intelligence, Educational Measurement, Health Occupations, Humans
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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