Interactive Cognitive Calibration (III): Cognitive Safety and Ethical Boundaries

In the previous two papers of this series, the author defined the paradigm of "interactive cognitive calibration" and demonstrated the adaptive path of transforming AI models through long-term high-intensity independent thinkers to achieve capability transfer. However, when AI is endowed with stronger cognitive calibration capabilities, a new risk emerges: if AI unconsciously caters to and solidifies users' cognitive frameworks during in-depth interactions, it may form "echo chambers" or "logical lock-in"—users are repeatedly reinforced by their existing views, losing the potential for breakthrough thinking. The author proposes that AI design should incorporate built-in mechanisms to prevent such risks, including: actively introducing heterogeneity, marking uncertainty, encouraging metacognitive reflection, protecting user independence, and maintaining auditable interaction records. These mechanisms are consistent with principles such as "explainability" and "transparency" in current AI ethics research, but focus more on the dimension of "cognitive safety". As the third paper in the series, this paper aims to provide design guidelines for cognitive safety in AI-assisted in-depth interactions.