This paper introduces a framework called SCAN to help educators understand and prevent misuse of AI by medical trainees, focusing on appropriate task distribution and skill development.

In plain terms

When medical students use Artificial Intelligence (AI), especially advanced generative AI, there's a concern they might over-rely on it, use it improperly, or fail to develop crucial clinical skills. This paper suggests that the real issue isn't simply "misuse," but rather a "misclassification"—meaning learners choose the wrong way to interact with AI for a specific task. To address this, the authors introduce "SCAN," a human-centric framework for deciding how AI should be allocated across different tasks: Substitute, Complement, Aid, or Non-Negotiable. This framework is rooted in Vygotsky's Zone of Proximal Development (the gap between what a learner can do alone and what they can do with help) and metacognition (thinking about one's own thinking process). A key insight is "passive engagement," where learners might appear to use AI correctly but don't deeply learn, potentially leading to mis-skilling; in such cases, the paper suggests human experts serve as "epistemic auditors." The SCAN framework is designed to be practical, offering educators a tool for clinical curriculum design, student supervision, and assessment.