Artificial Intelligence as a Cognitive Mirror A Methodological and Scientific Response to the Hypothesis of Models' Overestimation of Human Rationality

This paper provides a methodological and architectural response to recent claims that large language models (LLMs) systematically overestimate human rationality in strategic and game-theoretic settings. Rather than treating this phenomenon as an intrinsic failure of model reasoning, the study argues that it emerges from design assumptions, normative training distributions, and the absence of explicit user modeling mechanisms.The paper introduces the concept of Artificial Intelligence as a Cognitive Mirror, framing model behavior as a reflection of designer priors, data realism, and user cognitive level. It distinguishes clearly between a model’s inference capacity and its ability to diagnose and adapt to human cognitive variability—two dimensions that are often conflated in current evaluations.To address this gap, the work proposes a multi-level training and interaction framework that incorporates stratified representations of human reasoning (from intuitive and error-prone to analytically strategic), an explicit error modeling layer, probabilistic user classification, and temporal updating through moving error averages. This architecture enables dynamic identification of user cognitive level, adaptive interaction over time, and the construction of a quantitative cognitive trajectory for each user.Rather than contradicting recent empirical findings, the paper complements them by offering an architectural explanation for why partial adaptation in current models remains limited. It argues that developing more realistic and human-aligned AI systems requires moving beyond purely normative inference toward diagnostic, user-aware, and temporally adaptive architectures.This work is intended for researchers in artificial intelligence, cognitive science, human–AI interaction, and behavioral modeling, and is suitable for use as a conceptual and methodological reference in future AI system design.