This work introduces AI Fluency Through Stylometric Autonomy, a field‑level framework that expands traditional AI literacy to address the infrastructural pressures predictive systems exert on human writing. Rather than treating AI as a tool or a plagiarism risk, the framework examines how predictive smoothing, stylometric drift, and embedding‑space clustering reshape voice, cadence, and conceptual identity. It defines stylometric autonomy as the core competency required for authorship in AI‑mediated environments and provides educators and institutions with a diagnostic architecture for recognizing when student writing is being subtly overwritten by algorithmic patterns. This document consolidates the conceptual foundations of the SignalRupture canon and establishes a vocabulary for understanding and preserving human distinctiveness in a predictive world.
Signal Rupture (Thu,) studied this question.