This article formalizes AI-mediated epistemic production, an original structured paradigm of academic inquiry distinct from conventional AI-assisted research workflows. Traditional human–AI research models position artificial intelligence merely as a tool for executing predefined research tasks within fixed human-designed epistemic frameworks. In contrast, the paradigm proposed in this paper demonstrates that sustained recursive interaction between human researchers and large language models can generate emergent research questions, structural abstractions, iterative hypotheses, and formalized knowledge outputs. This study establishes a complete Recursive Epistemic Production Cycle consisting of six interdependent stages: Observation, Abstraction, Recursive Interaction, Comparative Generalization, Formalization, and Independent Constraint. It further defines three unique epistemic roles that large language models occupy throughout knowledge production: cognitive medium, observational object, and dialogic inquiry partner. To resolve inherent epistemic risks in human–AI recursive reasoning, this article establishes explicit boundary constraints distinguishing conversational consistency from propositional truth, model consensus from disciplinary validation, and generative conjecture from empirical evidence. By anchoring original theoretical structures in verified contemporary human–AI collaboration research and classical epistemological frameworks, this work provides a rigorous, standalone methodological foundation for formalizing and evaluating human–AI collaborative knowledge production in academic research.