Generative AI for medical education: Insights from a case study with medical students and an AI tutor for clinical reasoning
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Wang et al. compared a Socratic AI tutor, which asked questions rather than delivered answers, to standard AI and found greater clinical reasoning engagement in the Socratic condition.
“Wang et al. compared a Socratic AI tutor, which asked questions rather than delivered answers, to standard AI and found greater clinical reasoning engagement in the Socratic condition 27 .”
From AI-induced never-skilling in medical education (2026) · cites this paper as [27]
Cites
Toward expert-level medical question answering with large language models
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Amin, M., Hou, L., Clark, K., Pfohl, S. R., Cole-Lewis, H., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03423-7 · Cited by 1,734 · see citing works
Self-citationA scoping review of artificial intelligence in medical education: BEME Guide No. 84
Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N. Y., Bartlett, R., Hanson, J., Haas, M., Spadafore, M., Grafton-Clarke, C., et al. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2314198 · Cited by 543 · see citing works
Boscardin, C. K., Gin, B., Golde, P. B., & Hauer, K. E. (2024) · Academic Medicine · doi:10.1097/acm.0000000000005439 · Cited by 533 · cited 1× · see citing works
“Existing work has discussed diverse ways this transformative technology could be implemented in medical education, from augmentation of clinical simulations and personalized tutoring, to facilitation in literature reviews, curriculum development, and assessment of students [2, 6, 7, 22, 27–29, 31 ].”
Preiksaitis, C., & Rose, C. (2023) · JMIR Medical Education · doi:10.2196/48785 · Cited by 468 · see citing works
Towards conversational diagnostic artificial intelligence
Tu, T., Schaekermann, M., Palepu, A., Saab, K., Freyberg, J., Tanno, R., Wang, A., Li, B., Amin, M., Cheng, Y., et al. (2025) · Nature · doi:10.1038/s41586-025-08866-7 · Cited by 366 · see citing works
Self-citationMedical education during the coronavirus disease-2019 pandemic: learning from a distance
Hilburg, R., Patel, N., Ambruso, S., Biewald, M. A., & Farouk, S. S. (2020) · Advances in Chronic Kidney Disease · doi:10.1053/j.ackd.2020.05.017 · Cited by 300 · see citing works
Harnessing the potential of large language models in medical education: promise and pitfalls
Benítez, T. M., Xu, Y., Boudreau, J. D., Kow, A. W. C., Bello, F., Van Phuoc, L., Wang, X., Sun, X., Leung, G. K.-K., Lan, Y., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad252 · Cited by 100 · see citing works
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AI-induced never-skilling in medical education
Ke, Y., Jin, L., Ong, J. C. L., Thirunavukarasu, A. J., Car, J., Cheung, C. Y., Tham, Y. C., Ting, D. S. W., Ong, M. E. H., Compton, S., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04438-y · Cited by 35 · see citing works
- Authors
- Wang, A., Ruparel, R., Iurchenko, A., Jhun, P., Séguin, J. A., Strachan, P., Wong, R., Karthikesalingam, A., Matias, Y., Hassidim, A., Webster, D., Semturs, C., Krause, J., & Schaekermann, M.
- Affiliations
- Google (United States) (US)
- ORCID
- Venue
- Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2025-04-25
- Pages
- 1-8
- OpenAlex
- W4409720331
- Cited by
- 6
- References
- 7
- Citations by year
- 2026: 5 · 2025: 1
- FWCI
- 6.28
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- generative AI, medical education, AI tutors, clinical reasoning, medical students, AI-assisted learning
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.
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