Leveraging AI to Democratize the Hidden Curriculum in Medical Education: An Implementation Framework

TL;DR

This paper proposes using AI to help medical students from underrepresented backgrounds access unwritten rules and expectations in medical training.

Contribution

A novel theoretical framework and implementation strategy for using AI to address inequities in medical education's hidden curriculum.

Findings

AI can reduce cognitive burdens on disadvantaged students by supporting professional acculturation.

The framework integrates cognitive load theory and the Fast/Slow Thinking paradigm to explain and address educational inequities.

Thoughtful AI implementation can help build a more inclusive medical workforce.

Abstract

The “hidden curriculum” in medical education—comprising unwritten rules, values, and expectations—significantly impacts student success, yet remains inaccessible to students from underrepresented backgrounds. This paper presents a theoretical framework and practical implementation strategy for using artificial intelligence (AI) to democratize access to this hidden curriculum. We analyze how cognitive load theory and the Fast/Slow Thinking paradigm explain inequities in professional integration, then propose a comprehensive implementation approach to guide equitable AI integration. This model demonstrates how AI tools, when thoughtfully implemented, can reduce cognitive burdens on disadvantaged students, accelerate professional acculturation, and contribute to building an inclusive medical workforce.