Root Knowledge: Embodied Knowledge as a Foundation for Coherent Human–AI Interaction

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From the beginning of communication and scientific expression, humans have built systems of information to organize lived experience, reduce uncertainty, and transmit knowledge across time. Neuroscience now increasingly explains the body, brain, and self through interoception, prediction, embodiment, neurophenomenology, and spatiotemporal accounts of psychopathology, yet it remains unclear how these layers interact to produce a coherent self-narrative. In parallel, artificial intelligence has advanced from computation to large-scale generative systems capable of organizing information and producing precise outputs, but it remains largely disconnected from how such outputs are integrated by the human system. This gap matters. Easy access to fast information, while one of the great achievements of modern society, is also exposing a crisis of fragmentation in mental health: divided attention, emotional overload, distorted perspective, and self-narrative disruption across individual and collective systems. Grounded in Root Frequency Theory, this paper proposes a coherence-centered model of human-AI interaction in which artificial intelligence is designed not only to generate faster or smarter output, but to scaffold information in ways that support meaning-making, self-narrative continuity, and integration across physical, biological, neural, and symbolic layers. The central question is: how can artificial intelligence support a deeper understanding and integration of the human intelligent system?