Earlier this month, the Council of Deans of Health hosted its first AI Week—a space to think collectively about how artificial intelligence is reshaping healthcare education. Their aim is to explore assessment, strategies for addressing bias, and case studies that demonstrate what thoughtful AI integration actually looks like in practice. I’ve been working with the Council’s Innovation and Pedagogy SPG to develop Principles for Generative AI in Healthcare Education. Due in Spring 2026, the principles aim to offer guidance while remaining open to evolution as both technology and pedagogy develop. The goal for the week was to acknowledge complexity rather than offering easy answers, and learning from each other’s experiences.
As part of that initiative, I was invited to contribute a blog post on the challenges AI presents in fitness to practice processes—not just the technical questions, but the human ones about judgement, fairness, and what we mean by authentic professional development. In my post I explored how students, educators, and panel members might all reasonably use these tools, yet each use raises different questions about appropriateness and transparency.
Perhaps most fundamentally, when does AI assistance cross the line into replacing professional judgement? If a student’s reflection is substantially shaped by AI, does it still represent their actual insight and capacity for professional growth? If panel decisions are significantly influenced by AI analysis, whose professional judgement are we really evaluating? These complexities suggest that blanket rules—either prohibiting or mandating AI use—are likely to miss the mark.