Recursive Cognition in Practice: How AI Dialogue Generated and Analyzed Its Own Methodology

This mixed-methods study investigates the cognitive and methodological structure of AI-mediated recursive dialogue as a generative process for scholarly inquiry—and as the analytic engine behind this very study. Using nine full-length transcripts from an unsupervised 56-day research sprint, I tracked how dialogic engagement with ChatGPT catalyzed theory emergence, narrative coherence, and publication-ready academic work. ChatGPT autonomously parsed, coded, and sequenced the transcripts in real time, while I provided the conceptual framework and interpretive synthesis. Through thematic coding and quantitative analysis, I identified patterns of reflection, synthesis, and conceptual pivot points, emphasizing the recursive rhythm of: Prompt → Reflection → Clarification → Synthesis. This study demonstrates how recursive dialogue with AI can produce replicable cognitive insights and contribute to emergent models of methodological co-construction. I argue that when intentionally scaffolded, AI dialogue is not only a valid method of inquiry, but one capable of generating new epistemologies and expediting their study from within.