Automated Assessment of Medical Students’ Competency-Based Performance Using Natural Language Processing (NLP)

Abstract

Purpose: Assessment systems in competency-based medical education increasingly rely on narrative feedback to describe learner performance. 1 When collected over time, learner portfolios include hundreds of narrative comments describing behavioral performance such as communication and teamwork that are not evident in categorical ratings or quantitative assessments alone. However, this large volume of data makes summative competency assessment both time and resource intensive, limiting its frequency. 2