Cukurova's framework reveals most human-AI interactions aren't true collaboration

Mutlu Cukurova's new paper on arXiv challenges the broad labeling of AI interactions as 'collaboration.' Returning to decades of learning science research, Cukurova reconstructs the requirements that define true collaborative learning: partly symmetric and negotiated relationships, shared and negotiable goals, low and shifting division of labor, interactive synchronous exchange, mutual modeling, grounding, and socially shared regulation. Reviewing empirical studies of writing and problem solving, the paper concludes that current human-AI interaction typically operates as consultation, governance, delegation, or instruction—not collaboration.

To make these distinctions functional, Cukurova introduces a five-level diagnostic taxonomy of human-AI teaming: transactional, situational, operational, praxical, and synergistic. Each level is defined by the affordances the AI system exhibits. Only the highest level—synergistic—begins to satisfy the conditions the tradition places on collaboration. The paper argues that most required functions are present-day engineering choices, not future capabilities. It sets out implications for research, measurement, and responsible practice, urging designers to build AI systems with shared goals, negotiable roles, and mutual modeling to enable genuine human-AI collaboration.