AI Epistemology Revisited: From Neural Networks to Thought-Pattern Structures (Experience-Based AI Epistemology)

This paper presents an epistemological interpretation of artificial intelligence outputs by focusing on the interaction between human cognitive structure and AI systems, rather than on internal computational mechanisms. Based on sustained experiential observation across writing, conceptual structuring, and public archiving practices, the study argues that AI does not generate meaning autonomously, but amplifies structurally organized human thought. To account for this phenomenon, the paper introduces the concept of the Thought Pattern Network (TPN), which describes human thought as an internally organized network of meaning relations that functions as a latent selection framework in AI-mediated environments. From this perspective, the consistency, depth, and continuity of AI outputs reflect the coherence and structural integrity of human cognition embedded in the input. This work reframes AI not as an autonomous agent of meaning, but as a structural resonance system that magnifies existing cognitive patterns. It proposes an epistemological shift in understanding AI, emphasizing that philosophical cognition remains a foundational condition for epistemic interaction in the AI era. This paper is available as a preprint on arXiv.