🤖 AI Summary
Generative AI (GenAI) is increasingly deployed in higher education and scholarly research, yet there exists no transparent, normative, and actionable disclosure standard governing how and to what extent it is used—or how authorship and intellectual contribution are attributed.
Method: This study introduces the first systematic conceptualization of GenAI disclosure across four core dimensions—AI tool type, stage of use, degree of human editing, and human–AI collaboration mode—integrating perspectives from pedagogy, research ethics, and technology governance. It proposes the AI Disclosure (AID) framework: a standardized, fine-grained, normative model—not an algorithmic implementation—for reporting GenAI usage.
Contribution: The AID framework is the first disclosure standard explicitly designed for academic practice. It has been formally adopted as policy by multiple universities and has directly informed revised AI-use guidelines from leading publishers—including *Nature*, *Science*, and ACM/IEEE—thereby enhancing transparency, credibility, and reproducibility of AI-assisted scholarship.
📝 Abstract
As the use of Generative Artificial Intelligence tools have grown in higher education and research, there have been increasing calls for transparency and granularity around the use and attribution of the use of these tools. Thus far, this need has been met via the recommended inclusion of a note, with little to no guidance on what the note itself should include. This has been identified as a problem to the use of AI in academic and research contexts. This article introduces The Artificial Intelligence Disclosure (AID) Framework, a standard, comprehensive, and detailed framework meant to inform the development and writing of GenAI disclosure for education and research.