Dialogic Durability: How Human–AI Engagement Stabilizes Speculative Knowledge

This paper investigates how speculative ideas without empirical grounding or formal schematics can acquire epistemic functionality through recursive human–AI engagement. Using a dialogic methodology, the work demonstrates how interpretive structures—such as symbolic scaffolds, visual analogies, and conceptual mappings—gain stability over time through iterative rephrasing, constraint negotiation, and abstraction refinement between a human researcher and an AI language model.Instead of considering functionality as empirical implementation or code-operationality, the paper redefines it as epistemic robustness—the ability of an idea to maintain interpretive coherence in recursive returns. This is an exercise within philosophy of science, bringing together Popper, Kuhn, Lakatos, Peirce, and recent post-formalist theories of knowledge. It yields a new explanation of the origin of symbolic functionality without code, experiment, or mathematical proof, in persistent symbolic interaction with generative AI.The paper also includes a supplementary file of epistemic traces—dialogic excerpts and iterative revisions—that serve as artifacts of the stabilization process. These materials are not presented as empirical validation but as a demonstration of recursive conceptual emergence.This piece makes a contribution to discussion in epistemology, human–AI collaborative writing, and post-formalist modeling by providing a novel approach to stabilizing speculative knowing without technical implementation.