When Knowledge Breaks: Threshold Dynamics of Epistemic Drift in Human–AI Pipelines

Abstract

The integration of artificial intelligence into scientific workflows introduces a new layer of epistemic transformation between knowledge generation and decision-making. While AI-mediated representations can facilitate comprehension and scalability, they also create the potential for epistemic drift—systematic deviation of downstream interpretations and decisions from the original knowledge. Here, we present a framework combining three structural indices—Conceptual Density Index (CDI), Transitional Nonlinearity Index (TNI), and Knowledge Integration Index (KII)—with the Epistemic Transfer Tensor (ETT) to quantify knowledge stability across four stages: original text, AI representation, alternative human interpretation, and decision abstraction. Applying this framework to a curated dataset of 12 theoretical and astrophysical research articles, we find that epistemic drift exhibits threshold-like behavior: when TNI exceeds ~0.80, both drift (Δ(AI→D)) and the Transfer Risk Index (TRI) increase sharply. High KII mitigates instability, while CDI has negligible predictive value. Notably, the largest deviations occur during the AI-to-decision transition, highlighting that operationalization of knowledge amplifies structural distortions. These findings reveal that knowledge stability is governed not by volume, but by the complexity of conceptual transitions and internal integration. Our results provide empirical support for a phase-transition model of epistemic systems and suggest structural metrics as critical tools for predicting AI-mediated knowledge reliability, with implications for scientific communication, decision-making, and AI deployment in high-stakes domains.

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