Socratic Method Revisited: Human-AI Dialogue for Knowledge Creation and Internalization

Abstract

This paper presents the Revisited Socratic Knowledge Interaction Framework, a novel approach to human-AI collaboration in knowledge-intensive tasks. Leveraging the Socratic method, the framework systematically integrates classical Socratic elements—Elenchus (critical refutation), Maieutics (knowledge elicitation), Aporia (constructive doubt), and Dialectic (collaborative synthesis)—into human-LLM interactions. We delineate how AI augments select Socratic components, while humans retain final authority over context and ethics. Phase-guided dialogue turns confident-but-wrong outputs into testable claims that must be justified, challenged, and revised. The work describes an agentic AI model that orchestrates role-specific agents to collaboratively facilitate Socratic dialogue with users. Our full open-source implementation is publicly available on GitHub along with a live web application. Through practical scenarios in academic research and entrepreneurial planning, we demonstrate the framework's utility in fostering deeper inquiry, critical thinking, and knowledge co-creation by challenging assumptions and promoting iterative refinement. This approach reinforces an augmented intelligence paradigm by operationalizing human-in-the-loop oversight via phase-routed dialogue, explicit check-backs, and an auditable trail of reasons, leading to more trustworthy and superior co-created outcomes.