Leveraging artificial intelligence to accelerate competency development in diagnostic reasoning of medical students
Chatbot-enabled instruction accelerated and enhanced illness script development compared with conventional methods, operationalizing deliberate practice principles and making non-analytical reasoning teachable. By integrating structured pedagogy with scalable AI feedback, this approach reduces educator burden while fostering diagnostic expertise. Future multi-site studies should examine long-term retention, clinical transfer, and AI feedback fidelity to ensure reliability and equity.
Authors
- Moushumi Sur
- Peter Boedeker (ORCID: https://orcid.org/0000-0002-0879-5886)
- Winston Liaw (ORCID: https://orcid.org/0000-0002-7865-1685)
- Kanokpan Ruangnapa (ORCID: https://orcid.org/0000-0003-4853-7790)
- Supaporn Dissaneevate (ORCID: https://orcid.org/0000-0001-5012-0463)
- Satid Thammasitboon (ORCID: https://orcid.org/0000-0001-6896-092X)
- Shelley Kumar
- Araya Yuenyongviwat
- Brian Rismiller
Institutions
- Prince of Songkla University (TH)
- Baylor University (US)
- Baylor College of Medicine (US)
- Houston Fertility Institute (US)
- Texas Children's Hospital (US)
Publication Details
- Journal
- Medical Teacher
- Published
- 2026-03-17
- DOI
- https://doi.org/10.1080/0142159x.2026.2641769
- Primary Topic
- Clinical Reasoning and Diagnostic Skills
- Type
- article
- Field-Weighted Citation Impact
- 0.00