AI Fluency Through Stylometric Autonomy

AI Fluency Through Stylometric Autonomy presents a comprehensive institutional framework for understanding how generative AI reshapes student writing, authorship, and identity. Moving beyond traditional AI literacy, the document introduces stylometric autonomy as the core competency required for maintaining voice and authorship in predictive environments. It outlines the mechanisms through which AI influences human writing — including stylometric drift, predictive smoothing, embedding‑space identity, and saturation — and provides educators with a diagnostic model for recognizing when student work is being overwritten by algorithmic patterns. The framework includes a competency model, curriculum integration strategies across K–12 and higher education, a diagnostic guide for assessing authorship integrity, and a teacher‑training module. An illustrative example demonstrates how students can learn to identify predictive smoothing without revealing operational methods. The packet concludes with a canon paragraph situating the work within the broader SignalRupture field, framing AI‑mediated writing as an infrastructural challenge rather than a technical one. Overall, the document establishes a field‑level architecture for preparing institutions to navigate authorship, identity, and voice in the AI era.