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.

翻译：“协作”这一概念已被迅速扩展，用以描述人们当前与对话代理、智能导师、自适应平台以及生成式人工智能工具之间的互动。本章探讨了将学习科学中这一严谨概念如此泛化应用所带来的得与失。通过回归协作学习的长期理论框架，本章重构了情境、交互及一系列认知过程在历史上被视为协作所需满足的条件。人机协作需要一种部分对称且可协商的关系、共同且可协商的目标、低且动态变化的任务分工、交互式同步交流，以及相互建模、共同理解和社会共享调节。通过梳理涉及写作与问题解决的过程敏感性实证研究，本章指出当前大多数人机交互更适合被描述为咨询、治理、委托或指导，而非协作。为使这些区分具有实用性，本章引入了一个五级人机协同诊断分类体系（即交易级、情境级、操作级、实践级与协同级），该体系由人工智能系统展现的可供性定义。研究表明，仅最高层级开始满足传统上对协作所设定的条件。本章推导出人工智能系统为达成协作所必须拥有的功能，论证其中大部分是当前工程选择而非有待实现的能力，并阐述了这对教育领域人机协作的研究、测量与负责任实践的意义。