用认知框架指导AI任务分配，让人类更好发挥与AI协作优势。
SCAN: A Decision-Making Framework for Effective Task Allocation with Generative AI
- 提出四类任务子区：替代、互补、辅助、不可协商，分类AI使用场景。
- 通过元认知扫描，帮助学习者和职场人科学决策是否用AI完成任务。
- 适合教育、职场中希望实现人机协同与终身学习的用户参考。
我们提出SCAN——一种以人类为中心的决策框架，基于维果茨基的最近发展区与元认知理论，指导学习者在生成式人工智能（GenAI）环境下进行有效任务分配。在SCAN中，我们系统化地形式化了人机交互，引入四种“子区域”：替代、互补、辅助、不可协商。描述四类子区域后，展示了该框架如何应用于职场知识工作者与学生群体，使其能元认知地“扫描”自身对GenAI的使用。进一步讨论了该框架与认知负荷理论、认知卸载、奉承行为、人机交互的三种决策模式（自动化、增强、协作）、未来工作中的技能提升与技能退化等问题的关联，并兼顾了人际与人机学习。我们认为，SCAN为讨论GenAI是补充还是取代人类能力提供了起点，总体目标是维持终身学习，具体目标是实现混合智能。
原文摘要 · Abstract (English)
We introduce SCAN -- a human-centric decision-making framework to facilitate learners for effective task allocation with Generative Artificial Intelligence (GenAI) based on Vygotsky's Zone of Proximal Development and Metacognition. In SCAN, we systematize and formalize AI-human interaction by introducing a task-identification approach with four "sub-zones": Substitute, Complement, Aid, and Non-negotiable. After describing the four sub-zones, we demonstrate how SCAN framework can be applied for knowledge workers in the workplace and students in education to metacognitively "scan" their use of Generative AI. We then discuss how such framework can be related to cognitive load theory, cognitive offloading, sycophancy, three decision-making modes in human-AI interactions (automation, augmentation, and collaboration), future of work such as upskilling and deskilling, and how it accounts for both human-human and human-AI learning. We propose that SCAN offers a great starting point before discussing whether GenAI complements or replaces our abilities when completing a task, with a general objective of sustaining lifelong learning, and a specific goal of reaching hybrid intelligence.
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