🤖 AI Summary
The widespread adoption of AI writing tools has blurred the boundaries of authorship, accountability, and intellectual contribution, while existing disclosure practices often lack transparency regarding the specifics of AI involvement. This study proposes a multidimensional, fine-grained attribution framework that systematically characterizes AI-assisted writing across document and paragraph levels through three core dimensions—form, generation, and evaluation—further extended by intent, control, and traceability. Employing a structured modeling paradigm integrated with metadata annotation and hierarchical description mechanisms, the framework enables, for the first time, operational and scalable recording of both AI intervention points and human oversight actions. Empirical validation through real-world writing cases demonstrates that this approach establishes a practical, transparent baseline for attributing contributions in AI-assisted text production.
📝 Abstract
Artificial intelligence systems are increasingly integrated into writing processes, challenging traditional notions of authorship, responsibility, and intellectual contribution. Current disclosure practices usually indicate whether AI was used, but rarely explain how it was used, where it intervened, or how its output was reviewed. This paper proposes a faceted model for representing AI-assisted text production at the levels of documents, chapters, sections, and paragraphs. The proposal introduces a core model based on Form, Generation, and Evaluation, and an extended model that adds Intent, Control, and Traceability. The model is positioned as a minimal operational baseline with extensibility toward higher-fidelity representations. A worked example based on the production of this article demonstrates applicability.