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

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.