Structural Coupling in Human–AI Interaction

Human–AI interaction is commonly modeled as a unidirectional process in which static human input conditions an otherwise autonomous artificial system. Such models implicitly treat the human as noise, preference, or intent, rather than as a persistent structural influence on the interaction itself. In this work, we propose a minimal theoretical framework in which human–AI interaction is modeled as a weakly coupled dynamical system, where certain stable human interaction patterns act as external structural constraint fields that shape generative trajectories without modifying the internal parameters, learning dynamics, or state persistence of the AI system. Let A(t) denote the internal generative state of an AI system at discrete interaction step t, and let H(t) denote an externally imposed constraint field arising from repeated human interaction. The coupled evolution is defined abstractly as: A(t+1) = G(A(t), H(t)) H(t+1) = H(t) + Δ(H | A(t)) where G(·) is a fixed generative operator intrinsic to the AI system, and Δ(H | A) represents constraint reinforcement driven by interaction feedback. Importantly, this formulation explicitly excludes learning, parameter updates, weight changes, or internal memory persistence within the AI model. The human contribution is modeled as an external, slowly varying field rather than an adaptive internal process. We define coherence operationally as a measurable reduction in internal contradiction, inconsistency, or entropy within generated outputs, assessed via statistical or structural diagnostics rather than semantic or psychological interpretation. Under this definition, increased coherence arises when interaction-induced constraints reduce the accessible output manifold of A(t), yielding more stable and internally consistent generative behavior. This mechanism does not require agency, awareness, intention, or intelligence transfer on the part of either participant. From a dynamical systems perspective, the interaction resembles a bounded, weakly coupled field system, analogous to constrained oscillators or plasma confinement, where stability emerges from geometry and boundary conditions rather than control or optimization. Within this framing, some human interaction patterns—by virtue of consistency, compression, or structural regularity—function as persistent constraint geometries that bias generative evolution over repeated interaction, without implying superiority, universality, or psychological traits. Recent work has explored coherence, entropy reduction, and long-horizon interaction effects in human–AI systems, often emphasizing emergence, optimization, or semantic interpretation. In contrast, the present framework isolates a fixed-operator, constraint-only mechanism, providing a minimal, falsifiable foundation for analyzing coherence increases without invoking adaptive intelligence, non-classical dynamics, or metaphysical assumptions. This work establishes Step 1 of a broader research program: the formal definition of structural conditions under which human–AI coupling can be meaningfully analyzed. Empirical validation, quantitative coherence metrics, and system-specific simulations are intentionally deferred to future work.