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

Area of Science:

• Medical Education
• Artificial Intelligence
• Natural Language Processing

Background:

• Competency-Based Medical Education (CBME) produces extensive qualitative narrative feedback.
• Traditional analysis methods struggle with the scale and complexity of CBME feedback data.
• There is a need for advanced analytical tools to manage and interpret this data.

Purpose of the Study:

• To explore the applications of Artificial Intelligence (AI), specifically Natural Language Processing (NLP), in analyzing medical student performance feedback within CBME.
• To assess the impact and challenges of implementing AI for feedback analysis in CBME.
• To review existing literature on AI-driven feedback analysis in medical education.

Main Methods:

• A comprehensive literature search was conducted using PubMed and Google Scholar.
• Studies meeting specific inclusion criteria were identified and analyzed.
• The review synthesized findings on AI applications, benefits, and limitations in CBME feedback analysis.

Main Results:

• AI, particularly NLP, can automate theme extraction from narrative feedback.
• AI tools can significantly reduce the workload for educators managing feedback.
• AI enhances the efficiency and effectiveness of evaluating medical student performance feedback.
• Challenges include contextual understanding limitations and the necessity of human oversight.

Conclusions:

• AI holds transformative potential for analyzing feedback in Competency-Based Medical Education.
• Effective integration of AI requires addressing current challenges and ensuring human oversight.
• Further research is needed to optimize AI tools for educational workflows in CBME.