Disclosure is not documentation: an open science framework for documenting generative AI use in scholarly research and publication workflows

Background Generative AI is increasingly used in scholarly research, writing, and publication workflows. Many journal and publisher policies ask authors to disclose relevant AI use, but disclosure alone rarely clarifies how AI-assisted work was performed, what information was entered, how outputs were evaluated, or how human responsibility was maintained. This creates a gap between AI-use disclosure as a publication requirement and AI-use documentation as an open science practice. Methods This article develops a conceptual and practical framework for documenting generative AI use in scholarly workflows. The framework was informed by exploratory, non-systematic source and policy mapping, AI-assisted exploratory evidence mapping, manual review of selected recent literature, and development of accompanying Open Science Framework materials. These steps were used to identify recurring documentation expectations and unresolved policy gaps and to translate them into practical documentation domains, with particular attention to task specificity, proportionality, role-specific documentation, privacy-sensitive transparency, and clinically sensitive contexts. Results The framework distinguishes disclosure from documentation and proposes documentation fields for minimal and extended AI use. Minimal documentation is intended for low-risk uses such as limited language polishing, whereas extended documentation is recommended when AI supports literature synthesis, coding, analysis, interpretation, manuscript drafting, peer-review-related work, clinical material, or research procedures. The framework is accompanied by reusable OSF materials, including documentation templates, prompt-log structures, declaration examples, checklists, clinical redaction guidance, and source-tracking materials. Conclusions AI-use disclosure communicates that AI was used; documentation makes the AI-assisted workflow traceable, inspectable, and accountable. A task-specific, proportionate, role-specific, and privacy-sensitive documentation approach can support responsible AI use while protecting confidential, patient-related, peer-review-related, and methodologically sensitive information. The accompanying bilingual materials are openly available on OSF: https://doi.org/10.17605/OSF.IO/A439J .