Concept Mining Method

This manuscript proposes the Concept Mining Method, a human-led mode of thinking with conversational AI in which a user holds onto dissatisfaction left after an AI explanation, twists the direction of questioning, accumulates a dialogue narrative, and then sends a retrieval signal so that the repeated structure hidden in the dialogue can be brought back as a candidate concept. The method is not presented as a prompt-engineering technique for obtaining better answers, nor as a claim that AI itself philosophizes or autonomously creates concepts. Rather, it is a reflective method in which a human uses AI-generated explanations, comparisons, objections, structuring, and retrieval to clarify a concept-forming process that emerges from the human’s dissatisfaction, direction-sensing, and final judgment. The paper distinguishes single-session concept mining, parallel mining, manual agentic operation, context preservation, and post-retrieval evaluation. A central example is drawn from a dialogue about Erdős’s unit distance problem, where a mathematical explanation led to dissatisfaction about permitted operations, then to questions about criteria, the application of Criterion-Selection Theory, and finally to the possibility of Criterion-Power Theory. The manuscript also relates the method to adjacent discussions such as philosophizing with AI, Socratic dialogue, concept engineering, AI-assisted qualitative analysis, philosophical counseling, and human-AI co-creation. Finally, it connects the method to the author’s previous work on Criterion-Selection Theory, Criterion Violation Output, probabilistic natural-language interpreters, natural-language programming, and JDF/CVO/CSO as a post-retrieval evaluation framework.