A Control-Based Model of Authorship in AI-Mediated Research

Abstract The rapid integration of artificial intelligence (AI) into research and writing workflows has generated a crisis of authorship attribution. Existing frameworks often rely on binary distinctions human versus AI generated failing to capture the nuanced, iterative interactions that characterize real world usage. This paper proposes a control based model of authorship, arguing that authorship is not determined by initiation or mechanical generation, but by sustained conceptual control, evaluative judgment, and iterative refinement. By distinguishing between delegation, retrieval, and directed synthesis, this framework clarifies when AI functions as a tool versus when it meaningfully shapes intellectual output. The model may provide a defensible basis for human authorship in AI mediated research while acknowledging the non-trivial role of generative systems.