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  1. Home
  3. Ambient Ai Scribes To Create Educational Feedback Notes For Medical Students: Randomized Trial.
  1. Home
  3. Ambient Ai Scribes To Create Educational Feedback Notes For Medical Students: Randomized Trial.

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Related Experiment Videos

Ambient AI Scribes to Create Educational Feedback Notes for Medical Students: Randomized Trial.

Jaideep S Talwalkar1, David Chartash2, Lisa Zhang2

  • 1Departments of Medicine and Pediatrics, Yale School of Medicine, 367 Cedar Street, Bldg D, New Haven, CT, 06510, United States, 1 203-737-4190, 1 203-737-4199.

JMIR Medical Education
|May 28, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Ambient artificial intelligence (AI) scribes enhance medical education feedback quality without increasing instructor workload. This AI scribe-assisted workflow shows potential for transforming feedback documentation, despite minor inaccuracies requiring review.

Keywords:
AIambient scribeartificial intelligencecompetency-based educationfeedbackformative assessmentmedical student education

Related Experiment Videos

Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Clinical Competence Development

Background:

  • High-quality feedback is crucial for medical trainees' clinical competence and professional growth.
  • Faculty face challenges in providing written feedback due to documentation burden.
  • Ambient AI scribes offer a potential solution by capturing and structuring clinical interactions.

Purpose of the Study:

  • To evaluate ambient AI scribes in generating educational feedback for first-year medical students.
  • To assess the impact of AI scribe-assisted workflows on feedback quality and instructor effort.

Main Methods:

  • Randomized controlled trial comparing human-only and AI scribe-assisted feedback workflows.
  • AI scribe generated transcripts, summarized into notes using large language models, and edited by instructors.
  • Feedback quality measured by the Evaluation of Feedback Captured Tool (EFeCT); task load and usability assessed via NASA-TLX and SUS.

Main Results:

  • AI scribe-assisted feedback (human-edited and unedited) demonstrated significantly higher EFeCT scores than human-only feedback (P<.001).
  • AI-assisted outputs were longer than human-only narratives (P<.001).
  • AI-generated feedback had low rates of mischaracterization (6.8%) and hallucination (1.7%), with most errors corrected during editing; task load and usability were comparable between groups.

Conclusions:

  • Ambient AI scribe-assisted workflows improve written narrative feedback quality without increasing instructor effort.
  • This technology has the potential to revolutionize feedback documentation in medical education.
  • Careful review is necessary to address occasional inaccuracies in AI-generated feedback.