The Theory of Epistemic Symmetry: A Theory of Epistemic Transformation in the Age of Artificial Intelligence

Abstract

The history of epistemology—from the Socratic method to twentieth-century constructivism—is predicated on a structural asymmetry: inquiry (the formation of questions) is a generative, high-effort cognitive process that precedes and directs discovery (the retrieval of answers). This paper introduces and substantiates the Theory of Epistemic Symmetry, arguing that high-capability generative artificial intelligence (AI), specifically Large Language Models (LLMs) utilizing transformer architectures, fundamentally collapses this asymmetry. By enabling computational reversibility—the capacity to reconstruct complex prompts from outputs with high fidelity—AI transforms the question-answer relationship from a unidirectional vector into a bidirectional, symmetrical exchange. Utilizing a multi-stage theory-building methodology, I integrate findings from computer science, cognitive psychology, and social epistemology to propose a unified framework. I define five core constructs: Computational Reversibility, Question-Answer Equivalence, Epistemic Symmetry, Bidirectional Epistemology, and Epistemic Labor Collapse. Through a system of 33 falsifiable propositions, the theory predicts that as computational reversibility approaches unity, traditional epistemic labor is offloaded, professional expertise is redefined from knowledge possession to verification, and educational assessment systems based on output generation face a crisis of validity. I conclude that this shift represents not merely a change in tools, but a phase transition in the physics of knowledge production, necessitating a radical reimagining of pedagogy and professional practice.

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Key Contributions

Five Core Constructs

Implications