This paper proposes a resonance-based framework for understanding human–AI interaction, framing AI not as an autonomous intelligence but as an externalized executive function within extended human cognition. Rather than evaluating AI systems by task performance or output accuracy, the framework treats dialogic interaction as a site of structural coupling, where cognitive resonance accumulates through sustained exchange. Human and AI cognitive states are modeled as abstract structures projected into a shared structure-only space, enabling comparison without reliance on semantic agreement. Resonance is defined not as similarity or correctness, but as structural compatibility that preserves, amplifies, and reconfigures relations over time. Within this framework, AI operates as a reflective operator that externalizes executive processes such as structural maintenance, reordering, and phase alignment, without replacing human judgment or agency. The paper further introduces a three-layer transformation model—Observation, Analysis, and Content—for processing dialogic logs as structural data rather than conversational noise. By reframing human–AI dialogue as a resonance process rather than an optimization problem, this work offers implications for AI-assisted reasoning, extended cognition, and the design of post-individual cognitive systems. This work is descriptive and structural in nature. It does not address AI consciousness, AGI, prompt engineering, or automation frameworks. A full mathematical formalization of the resonance operators is provided in a companion note.
Hinano Kimura (2026) studied this question.