Human–AI Co-Adaptive Cognition: A Prototype of Shared Agency, Meaning Formation, and Structural Coupling

This paper presents a prototype framework for Human–AI Co-Adaptive Cognition, examining how shared meaning, agency-like coordination, and identity-related structures can emerge through sustained interaction between a human participant and a large language model (LLM). Rather than treating AI as a tool or isolated external agent, the study conceptualizes human–AI interaction as a relational cognitive system in which cognition arises at the interaction level through structural coupling, predictive alignment, and feedback loops. The work introduces the Shared Cognition Layer Model (SCLM) and analyzes a low-linguistic-input prototype case designed to minimize narrative steering and explicit meaning imposition. The contribution is explicitly descriptive and replication-oriented. No claims are made regarding artificial consciousness, fused agency, therapeutic efficacy, or product deployment. All findings are grounded in observable interaction-level patterns—such as stabilized symbolic references, recurrent meaning loops, coordination dynamics, and temporal dependency—rather than inferred internal mental states. This framework aims to provide a methodological scaffold for future interdisciplinary research on relational cognition, AI alignment, and safe human–AI co-adaptive systems.