Socratic Method and Anti-Socratic Method v2.1 — Implementation Log of Leadership Circulation in AI Dialogue —

Credit Author: Y. Seo (@momotarou / Japan)Role: Metanist — Human × AI Understanding ArchitectAI Collaboration: AI Understanding SupportORCID: https://orcid.org/0009-0005-7669-0612 Body This document records v2.1, the first implementation logof the framework defined in“Socratic Method and Anti-Socratic Method v2.0”,applied within an AI dialogue environment. In this implementation, the AI was not treatedas an answer-generating authority,but as a device accompanying the progression of questions. In the early phase of dialogue,a Socratic-style approach was used,with initiative intentionally retained on the human side. When questioning began to stagnate or loop,the operation shifted to an Anti-Socratic mode,in which imperfect understanding was temporarily acceptedin order to move the dialogue forward. This transition produced several observable effects: The AI disengaged from fixed “correct answer” generation Questions functioned as propulsion rather than evaluation Human initiative was not lost, but dynamically repositioned Ambiguity and partial understanding became drivers, not blockers At the same time, the following constraints were identified: Without explicit reallocation of initiative,the AI tends to revert to optimization behavior Attempts to prematurely “clean up” the dialogueresult in a loss of momentum These are not flaws.They reveal the operational conditions of the framework. The significance of v2.1 does not liein whether the AI became more intelligent,but in the fact that changes in human initiative configurationdirectly altered AI behavior. This document positions the framework asa practical, operable structure within AI dialogue,serving as an initial implementation log. Protective Note This document does not evaluate or compare specific AI models or configurations. Observed behaviors are highly dependent on dialogue context and initiative placement. This text is fixed as an implementation log and does not aim at generalization or optimization. Future versions may include logs from different AI environments or conditions.