Abstract Background Artificial intelligence (AI) marks an inflection point in medical education systems built on scarcity of resources. These designs privilege standardisation and recall‐heavy examinations over reasoning and adaptive expertise, defined as the capacity to apply knowledge flexibly in uncertain clinical contexts, producing learners who memorise content but struggle with ambiguity, integration across domains and decision‐making under pressure. Objectives To outline a conceptual roadmap for integrating AI into medical education that strengthens adaptive expertise, productive struggle and assessment integrity rather than eroding them. Methods Conceptual analysis using educational, assessment and cognitive science frameworks to contrast scarcity‐era logics with emerging AI capabilities and synthesise illustrative use cases. Results We describe how AI can scaffold knowledge acquisition and inquiry; support authentic practice via virtual patients and educator‐created, AI‐enabled teaching tools and reshape assessment through blueprint‐aligned items and predictive learning analytics. We highlight AI's double‐edged nature: risks of undermining integrity, promoting cognitive deskilling and bypassing productive struggle, defined as purposeful, scaffolded difficulty that feels effortful yet achievable and that strengthens long‐term learning. We propose enabling conditions: trust, transparency, structured difficulty, and deliberate cognitive redistribution, defined as intentional reallocation of cognitive work between humans and AI tools, which offloads routine lower‐yield tasks to machines to preserve and advance human judgement, values, relationships and professional identity formation. Conclusions AI will either accelerate superficial shortcuts or amplify humane, expert practice, depending on how pedagogy, assessment and culture are redesigned. Intentional alignment can reclaim time and cognitive space for the uniquely human work at the heart of education.
Chow et al. (2025) studied this question.