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.

翻译：我们提出SCAN——一种以人为中心的决策框架，基于维果茨基的最近发展区理论与元认知，帮助学习者借助生成式人工智能有效分配任务。在该框架中，我们通过引入包含四个"子区域"的任务识别方法（替代、补充、辅助、不可协商），系统化并形式化了人机交互过程。在阐述四个子区域后，我们展示了知识工作者如何在职场、学生如何在教育场景中应用SCAN框架对生成式AI的使用进行元认知"扫描"。随后讨论该框架与认知负荷理论、认知卸载、奉承效应、人机交互的三种决策模式（自动化、增强与协作）、未来工作形态（如技能提升与技能退化）的关联，以及它如何涵盖人与人、人与AI的学习过程。我们主张，在讨论生成式AI是补充还是取代人类完成任务能力之前，SCAN框架提供了重要的前置思考基点，其总体目标是维持终身学习，具体目标则是实现混合智能。