Human–AI Interaction as the Fundamental Unit of Knowledge: An Embedded Cognition Framework

This preprint proposes a unified framework for understanding how knowledge is generated in human–AI systems. The study introduces the concept of a human–AI interaction loop as the fundamental unit of knowledge generation. Within this loop, artificial intelligence produces structured outputs, while human cognition interprets and assigns meaning. Crucially, the framework argues that interaction alone does not constitute knowledge. Knowledge emerges only when such interaction is embedded within and constrained by the real world, which provides input, limits interpretation, and validates outcomes. The paper further examines how institutional systems, such as scientific publishing, function as real-world validation mechanisms, stabilizing knowledge within broader social and cognitive systems. This framework connects three levels: • interaction (human–AI loop) • environment (real-world constraints) • system (knowledge stabilization) The work contributes to ongoing discussions in artificial intelligence, cognitive science, and complex systems by reframing knowledge as an emergent, interaction-driven process. This preprint is part of a broader research program on human–AI cognition, including work on recursive knowledge circulation and distributed cognitive systems. Plain-language summary: This paper explains how knowledge is created when humans interact with artificial intelligence, and how real-world feedback determines whether that knowledge is valid.