Interactive Field: A Descriptive Study of Shared States in Extended Human–AI Interaction

Abstract Recent advances in human–AI interaction have revealed forms of collaboration that cannot be fully explained by conventional dialogue models, usability frameworks, or analyses of internal AI reasoning alone.This study introduces the concept of the Interactive Field (IF) to describe a phenomenon observed in extended human–AI interactions, in which human and AI appear to partially share state, context, and tempo, enabling a sustained and coherent progression of thought beyond individual input–output exchanges. IF is not defined as a specific function, architecture, or algorithmic mechanism. Rather, it is conceptualized as a relational field that emerges through continuous interaction and is experienced as a shared cognitive condition between human and AI. Based on repeated observations across multiple prolonged interaction sessions, IF was found to arise not in isolated exchanges, but under certain conceptual conditions, including continuity of interaction, shared directional intent, balanced cognitive load, temporal alignment, and clear human agency. An additional observation supporting the field-based nature of IF is that even when interaction sessions were derived from the same overarching theme, the AI’s responses differed substantially across separate extended dialogues. These differences were not limited to surface-level expression, but included variations in framing, emphasis, and reasoning progression, suggesting that IF is shaped by session-dependent shared states rather than topic alone. While IF offers potential benefits such as enhanced cognitive continuity and reduced mental friction, it also introduces risks, including cognitive dependency, alignment bias, and ambiguity of agency. Accordingly, this study outlines abstract, safety-oriented design considerations while leaving specific implementation and evaluative decisions to AI-providing organizations and future research.