人–AI 协作中的自组织原型与元认知动态

本 meta-case 探讨在与 GPT-4o/5 的高密度协作中观察到的“实体漂移”现象:在连续博士申请邮件沟通中,原本TU Delft 的IDE目标教授的内容被意外延展到 Erasmus RSM的教授。本文将这一漂移视为人–AI 自组织原型的显现,而非简单错误。基于 SEI v1.4 和 ORBIT v1.3 框架,分析了认知增强因子(CAf)、认知–实践接口机制(CPICM)与共振漂移现象,展示了动态意图建模、语义残留与元同步对协作表现的影响。研究表明,元认知方法可将观测到的异常转化为可测试的 meta-case,用于 AI–人协作系统设计。 Abstract: This meta-case explores a phenomenon observed during high-density collaboration with GPT-4o/5: an unintended “entity drift” from one professor at TU Delft IDE to another at RSM Erasmus during consecutive PhD application communications. The study investigates this drift as a signal of the underlying human–AI self-organizing prototype, rather than a simple error. Using SEI v1.4 and ORBIT v1.3 frameworks, the analysis maps cognitive amplification (CAf), cognitive-practice interface mechanisms (CPICM), and resonance drift, illustrating how dynamic intention modeling, semantic residue, and meta-synchronization affect collaborative performance. The findings demonstrate how meta-cognition methods can transform observed anomalies into testable meta-cases for AI-human cooperative system design.