The Dynamic Organization of Sustained Human–AI Cognition: From Construct-Level Change to Relational Structure

As generative artificial intelligence becomes a routine participant in writing, learning, information retrieval, analysis, decision making, and problem solving, the central question in human–AI cognition research is shifting. The issue is no longer only whether AI raises or lowers a psychological variable or task outcome, but also how human cognitive activity is organized internally under sustained AI use. This article develops a framework of the dynamic organization of cognitive activity that moves the analytic focus from changes in the levels of isolated constructs to changes in the relational structure among cognitive functions. The framework focuses on online cognitive activity on the human side rather than prespecifying the “human + AI” ensemble as a single cognitive agent, and distinguishes five relational dimensions of cognitive reorganization: execution locus, cognitive governance, representational reorganization, process organization, and reachable cognitive space. These dimensions are analytically distinguishable but dynamically coupled. The framework further proposes a path-specific recursive principle: the outcomes, costs, and experiences of a given interaction may do more than update overall trust; they may selectively reweight the probabilities of different forms of cognitive organization in future interactions. Five sets of testable propositions follow: the same amount of AI use can correspond to different cognitive organizations; immediate outcomes cannot exhaust the cognitive process or its subsequent trajectory; changes in AI-use intensity need not track changes in internal cognitive organization; expansion of cognitive space and displacement of human-originated pathways may coexist within the same process; and different sustained cognitive organizations should produce different distributions of cognitive practice opportunities, whereas longer-term differentiation in abilities, strategies, and habits remains a developmental prediction requiring longitudinal evidence. The contribution is not to relabel cognitive offloading, self-regulation, cognitive control, distributed cognition, or human–AI co-regulation, but to offer a relational-level framework for describing how human cognitive functions are reconfigured under sustained AI participation, together with corresponding methodological implications and a research agenda.