Human–AI Cognitive Mismatch: Toward a Framework for Thought Transformation in Iterative Interaction Systems

The integration of conversational artificial intelligence into everyday cognitive processes has shifted human–machine interaction from instrumental use to sustained cognitive engagement. While existing research primarily focuses on the capabilities of AI systems, less attention has been paid to how human cognition itself is transformed through iterative interaction with such systems. This paper introduces the concept of cognitive mismatch, defined as the structural discrepancy between human-generated thought in its raw, non-linear form and the stabilized outputs produced by AI systems. Through iterative feedback loops, this mismatch can both enhance cognitive clarity and introduce risks such as premature stabilization and externalization of judgment. Positioned as an exploratory framework rather than a formal theory, this work contributes to emerging discussions in cognitive science, philosophy of mind, and human–AI interaction.