Artificial Intelligence in Narrative Feedback Analysis for Competency-Based Medical Education: A Review.

Journal: Studies in health technology and informatics
Published Date:

Abstract

Competency-Based Medical Education (CBME) generates large volumes of qualitative data in the form of narrative feedback. Traditional qualitative analysis methods face limitations in managing this data's scale and complexity. This review explores the applications, impact, and challenges of Artificial Intelligence (AI), particularly Natural Language Processing (NLP), for analyzing and visualizing medical student performance feedback within CBME contexts. We conducted a comprehensive search of PubMed and Google Scholar databases, identifying key studies that met our inclusion criteria. Our findings highlight how AI can enhance traditional analysis methods by automating theme extraction, reducing educator workload, and improving feedback evaluation processes. The review also addresses challenges associated with AI implementations, including contextual limitations and the need for human oversight. We conclude by emphasizing AI's transformative potential in CBME while identifying critical areas for further research to ensure effective integration into educational workflows.

Authors

  • Sameer Asim Khan
    College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Health, Dubai, United Arab Emirates.
  • Jamal Taiyara
    College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Health, Dubai, United Arab Emirates.
  • Nabil Zary
    Institute of Learning, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Health, Dubai, UAE.
  • Farah Otaki
    Strategy and Institutional Excellence, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Health, Dubai, United Arab Emirates.