你说的人机协作是什么意思：实现协作所需的前提功能和可供性
What do you mean by human-AI collaboration: Prerequisite functions and the affordances needed to achieve it
AI总结：
本文基于学习科学对协作的严格定义，提出人机协作需满足对称关系、共享目标等条件，并通过五级诊断分类法指出当前人机交互多为咨询而非协作，最后推导出实现协作所需的功能。
摘要：
“协作”这一概念已被迅速扩展，用来描述人们现在与对话代理、智能导师、自适应平台以及生成式人工智能（AI）工具所做的事情。本章探讨了当学习科学中一个要求严格的概念被如此自由地应用时，会获得什么、失去什么。回归长期存在的协作学习理论，本章重构了情境、互动和一组认知过程在历史上必须满足才能被称为协作的要求。人机协作需要部分对称且协商的关系、共享且可协商的目标、低且动态变化的劳动分工、交互式同步交流，以及相互建模、共同基础和社会共享调节。通过回顾对写作和问题解决的过程敏感实证研究，本章表明当前大多数人机交互更适合描述为咨询、治理、委托或指导，而非协作。为使这些区分具有功能性，本章引入了一个五级人机协同诊断分类法（即交易性、情境性、操作性、实践性和协同性），由AI系统展现的可供性定义。研究表明，只有最高级别才开始满足传统对协作所设的条件。本章推导出AI系统为实现协作所必须拥有的功能，论证其中大部分是当下的工程选择而非待实现的能力，并阐述了对教育中人机协作的研究、测量和负责任实践的意义。
英文摘要：
The concept of 'collaboration' has been extended rapidly to describe what people now do with conversational agents, intelligent tutors, adaptive platforms, and generative artificial intelligence (AI) tools in general. This chapter asks what is gained and lost when a demanding concept from the learning sciences is applied so freely. Returning to long-standing accounts of collaborative learning, it reconstructs the requirements that a situation, an interaction, and a set of cognitive processes have historically had to meet before being called collaborative. Human-AI collaboration requires a partly symmetric and negotiated relationship, shared and negotiable goals, a low and shifting division of labour, interactive and synchronous exchange, and mutual modelling, grounding, and socially shared regulation. Reviewing process-sensitive empirical studies of writing and problem solving, the chapter shows that most current human-AI interaction is better described as consultation, governance, delegation, or instruction rather than as collaboration. To make these distinctions functional, the chapter introduces a five-level diagnostic taxonomy of human-AI teaming (i.e. transactional, situational, operational, praxical, and synergistic) defined by the affordances an AI system exhibits. It shows that only the highest level begins to satisfy the conditions the tradition places on collaboration. The chapter derives the functions an AI system must possess for collaboration to be achievable, argues that most of these are present-day engineering choices rather than capabilities to be awaited, and sets out the implications for research, measurement, and responsible practice of human-AI collaboration in education.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
