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.
Building similarity graph...
Analyzing shared references across papers
Loading...
Fenix Wiles
Film Independent
International Journal of Qualitative Methods
Film Independent
Building similarity graph...
Analyzing shared references across papers
Loading...
Fenix Wiles (Mon,) studied this question.
synapsesocial.com/papers/68d9051441e1c178a14f4924 — DOI: https://doi.org/10.1177/16094069251381709