Interactive Cognitive Calibration (II): The Theoretical Turn from Universalization to Adaptation

Based on the "interactive cognitive calibration" paradigm established in the first paper of this series, this paper explores whether the paradigm can be universally promoted to ordinary users. By analyzing the extreme specificity of this paradigm, the author demonstrates that universalization is inherently impossible—its prerequisites are irreproducible, calibration quality depends on human leadership, and the one-on-one in-depth dialogue structure cannot be scaled up. This conclusion contrasts with the optimistic expectations of "technological universalism" in current AI education applications. On this basis, the author proposes an alternative path of "adaptive transformation": instead of lowering the threshold for public use, we transform the AI model itself through continuous in-depth interaction between top-tier long-term independent thinkers and AI, achieving cognitive-level alignment and capability transfer. This path draws on technical ideas of "reinforcement learning from human feedback" and "model fine-tuning", but emphasizes the uniqueness of data sources—only high-cognitive-density interaction data has training value. As the second paper in the series, this paper aims to provide a theoretical framework for the application path of this paradigm.