Deciding How Much to Trust AI for Teaching and Assessment
Abstract
Case: The Feedback That Arrived Before You DidA program director (PD) reviews documentation feedback generated by an AI tool being piloted in the residency program.The system analyzed a postgraduate year 2's admission note and produced detailed suggestions: tighten the history of present illness, clarify the differential, strengthen the reassessment plan.The feedback is specific, well-organized, and ready to share.But the PD pauses.The AI didn't see the patient.It doesn't know this trainee's baseline, learning goals, or that she struggles with confidence rather than competence.Should this feedback go directly to the resident?Be reviewed first?Be discarded entirely?These are new questions facing many medical educators: not whether to use AI for teaching and assessment, but how much autonomy to grant AI, and for which tasks.
Fields of Study & Subjects
Intelligent Tutoring Systems and Adaptive Learning
Explainable Artificial Intelligence (XAI)
Online Learning and Analytics
Publication details
- Journal / source
- Journal of Graduate Medical Education
- Publisher
- Journal of Graduate Medical Education
- Volume
- 18
- Issue
- 2
- Pages
- 105–107
- Publication date
- 1 April 2026
- Language
- EN