AI作为队友：重新思考医学培训中的任务分配
AI as Teammate: Rethinking Task Distribution in Medical Training
- Behavioral AI Institute（行为人工智能研究所）
- KIMEP University（哈萨克斯坦管理经济战略研究院大学）
- Harvard Medical School（哈佛医学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出将医学培训中AI的问题从“误用”重构为“分类错误”，提出以人为中心的SCAN决策框架，为医学教育中AI的应用提供可检验的认知科学实证研究方向。
AI中文摘要：
将人工智能（AI），尤其是生成式AI，整合到医学培训中引发了人们对学习者过度依赖、误用以及基础临床能力被削弱的担忧。我们在决策层面提出了概念重构：问题并非误用，而是分类错误——这是一种在选择与子区域不匹配的AI交互模式时，实时元认知评估的机制性失效。我们借鉴基于维果茨基最近发展区和元认知的、面向生成式AI任务分配的以人为中心的决策框架“SCAN”（替代、补充、辅助、不可协商），通过提供AI在临床推理发展中作用的可检验解释，推进了围绕AI在医学教育中角色的新兴社会建构主义讨论。该框架对在临床学习环境中如何检测、缓解，更重要的是预防分类错误提出了可检验的预测。关于临床推理发展，我们展示了技能习得（技能提升）和技能失败的轨迹（技能失败三联征：技能退化、从未掌握技能、技能误用）如何在个体任务层面运作，而固定阶段、全队列的处理方式无法捕捉这些轨迹。我们进一步确定，在正确分类的AI支架式任务中的被动参与是一种特别隐蔽、难以检测的技能误用途径——这需要从AI辅助到专家辅助的子区域重新识别，其中人类专家作为认知审核者。本文将SCAN框架应用于临床课程设计、监督和评估，并开启了基于认知科学的实证研究议程。这种从“误用”到“分类错误”的范式转变并非语义层面的：它为教育者提供了明确的视角，明确了需要关注什么、评估什么以及干预什么。
英文摘要：
Integrating Artificial Intelligence (AI), particularly generative AI, into medical training has prompted concerns about learner over-reliance, misuse, and erosion of foundational clinical competencies. We propose a conceptual reframing at the decision level: the problem is not misuse but misclassification - a mechanistic failure of real-time metacognitive evaluation in selecting a subzone-inappropriate AI interaction mode. Drawing on "SCAN" (Substitute, Complement, Aid, Non-Negotiable), a human-centric decision-making framework for generative AI task allocation grounded in Vygotsky's Zone of Proximal Development and metacognition, we advance the emerging social-constructivist conversation around AI in medical education by offering a testable account of AI's role in clinical reasoning development. This framework yields testable predictions for how misclassification can be detected, mitigated, and, more importantly, prevented in the clinical learning environment. Regarding clinical reasoning development, we show how trajectories of skill acquisition (upskilling) and failure (the triad of skill failure: de-skilling, never-skilling, and mis-skilling) operate at the individual task level in ways that fixed-phase, cohort-wide treatments fail to capture. We further identify passive engagement within correctly classified AI-scaffolded tasks as a particularly insidious, detection-resistant pathway to mis-skilling - one requiring subzone re-identification from AI assistance to expert assistance, with human experts serving as epistemic auditors. The paper operationalizes SCAN for clinical curriculum design, supervision, and assessment, and opens an empirical research agenda grounded in cognitive science. This paradigm shift from misuse to misclassification is not semantic: it offers educators a clear perspective on what to look for, what to assess, and what to intervene on.
