This month, the IAMSE Publications Committee review is taken from the article titled Leveraging AI to Democratize the Hidden Curriculum in Medical Education: An Implementation Framework, published in Medical Science Educator (6 September 2025) by James Keith Martin II and Mercedes Byrd.
Recently, the Medical Science Educator published, under its Innovation section, a paper discussing the potential use of AI to address inequities in medical education. The paper emphasized the hidden curriculum—the unspoken rules, values, and expectations that affect student success in medical school but often remain invisible to first-generation, low-income, and underrepresented minority students. This somewhat unconventional application of AI has great potential to support students who otherwise must learn these rules through trial and error, thereby leveling access to critical professional knowledge.
In this paper, the authors propose a groundbreaking approach that uses AI as a tool for equity and inclusion in medical education. They emphasize AI’s potential to democratize access to professional knowledge that has traditionally been available to those with insider connections. These inequities can create disadvantages for students from underrepresented backgrounds, and the authors propose a framework for using AI interventions to target key areas of inequity. The article outlines how AI-driven solutions can help to normalize terminology, professional norms, and curriculum planning by applying both Fast/Slow Thinking paradigm and Cognitive Load Theory. By using AI to make implicit knowledge explicit, the authors show how these cognitive disparities can be reduced.
The proposed implementation framework describes how artificial intelligence platforms—such as ChatGPT, Claude, Copilot, and Google’s NotebookLM—can be integrated to support learners and trainees across multiple domains. These tools can provide fast-thinking support by offering real-time guidance on professional etiquette, clinical hierarchy, and academic decision-making. In parallel, they can facilitate slow-thinking processes through structured planning tools that assist with study strategies, research engagement, and long-term residency preparation. Importantly, AI platforms can enhance mentorship by offering continuous, 24/7 virtual guidance that complements, rather than replaces, traditional human mentorship. The framework also emphasizes equitable integration, with careful attention to data privacy, bias mitigation, and alignment with existing curricular standards to ensure responsible and inclusive use. The paper also discusses key challenges—ethical oversight, balancing human and AI roles, and trust-building—and stresses that AI should serve as a bridge, not a barrier, to mentorship and inclusion.
One limitation of the paper is the lack of student feedback on how these AI systems perform in practice and the absence of data showing how implementation impacts student performance. As more institutions adopt these frameworks, additional data can be gathered to evaluate their effects on student outcomes.
Conclusion: By embedding AI-driven guidance directly into existing learning systems, medical schools can transform the hidden curriculum from privilege to shared knowledge—helping every student, regardless of background, navigate medical education with confidence. This approach represents a promising step toward enhancing mentorship and academic advising through equitable AI integration.
Yuriy Slyvka, MD, PhD
Professor of Instruction
Department of Biomedical Sciences
Ohio University, Heritage College of Osteopathic Medicine
Athens, Ohio, USA