This preprint proposes a direction-oriented framework for understanding Human–AI interaction based on Thought Structure and the Thought Pattern Network (TPN). Rather than examining the internal computational mechanisms of large language models (LLMs), this study investigates whether the principle through which AI generates language within large-scale language spaces can also be applied to the Thought Pattern Network formed by human Thought Structure. Drawing upon repeated observations across both personal AI interactions and publicly available AI services, the paper proposes Human–AI Functional Alignment as a framework in which human directionality and AI generative capability become structurally aligned. It further explores the possibility that AI may function as an interface through which Thought Structure can be continuously transmitted. This paper serves as the starting point of an ongoing research program connecting Resonance Neural Network, Definition of Thought Structure, AI Epistemology Revisited, and Human–AI Functional Alignment toward sustaining human creativity in the AI era.
Eun Jung Lee (Fri,) studied this question.