AI-ssessment: Towards Assessment as a Sociotechnical System for Learning
Abstract
Two decades ago, the advent of competency-based medical education (CBME) marked a paradigm shift in assessment. Now, medical education is on the cusp of another transformation driven by advances in the field of artificial intelligence (AI). In this article, the authors explore the potential value of AI in advancing CBME and entrustable professional activities by shifting the focus of education from assessment of learning to assessment for learning. The thoughtful integration of AI technologies in observation is proposed to aid in restructuring our current system around the goal of assessment for learning by creating continuous, tight feedback loops that were not before possible. The authors argued that this personalized and less judgmental relationship between learner and machine could shift today’s dominating mindset on grades and performance to one of growth and mastery learning that leads to expertise. However, because AI is neither objective nor value free, the authors stress the need for continuous co-production and evaluation of the technology with geographically and culturally diverse stakeholders to define desired behavior of the machine and assess its performance.
Research Areas
