Asks what is gained and lost when 'collaboration' is applied freely to human-AI interaction. Argues true collaboration requires symmetric/negotiated relationship, shared goals, low and shifting division of labor, interactive exchange, and mutual modeling. Introduces a 5-level diagnostic taxonomy: Transactional, Situational, Operational, Praxical, and Synergistic. Only Synergistic satisfies full collaborative conditions. Most current human-AI interaction is consultation, governance, delegation, or instruction.
Introduces a 5-level taxonomy of human-AI teaming; most current AI interaction is consultation/delegation, not true collaboration.Key Findings
The chapter reconstructs the requirements that a situation, an interaction, and a set of cognitive processes have historically had to meet before being called collaborative, returning to longstanding accounts of collaborative learning.True 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 author shows that most current human-AI interaction is better described as consultation, governance, delegation, or instruction rather than collaboration.The chapter introduces a five-level diagnostic taxonomy of human-AI teaming — transactional, situational, operational, praxical, and synergistic — defined by the affordances an AI system exhibits, with only the highest level beginning to satisfy the conditions the tradition places on collaboration.The author argues that most of the prerequisite functions an AI must possess for collaboration are present-day engineering choices rather than capabilities to be awaited.The Five-Level Taxonomy
The taxonomy runs from transactional interactions (discrete, affordance-poor exchanges) up through situational, operational, and praxical teaming to synergistic collaboration, where evidence of collaboration should be sought in the trajectory of the human-AI unit rather than in output quality or user satisfaction alone. The high bar is not intended as a stick with which to beat existing systems but as a target that lets the field name what AI systems already do well without pretending it is the collaboration they do not yet achieve.
Implications for AI in Education
For Human AI Collaboration research and Teacher Role practice, the chapter warns against inflating a precise construct into a slogan: calling all useful human-AI interaction "collaboration" obscures what would actually be required. Because most required functions are engineering choices, designers of AI Tutoring and Collaborative Learning environments can treat the taxonomy as a specification of design targets — and researchers can measure collaboration in the trajectory of the human-AI unit rather than in user satisfaction alone, keeping the stronger meaning of the word worth preserving.
Connected Concepts
AI TutoringCollaborative LearningAffective TutoringTeacher AI CompetencySocratic AI DialogueHelp SeekingPedagogical AgentPedagogical LLM TrainingConnected Articles
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Mutlu Cukurova (2026). What do you mean by human-AI collaboration: Prerequisite functions and the affordances needed to achieve it. arXiv:2606.15509. Handbook of AI and the Future of Education (forthcoming).