Artificial intelligence and transparency: Toward a framework for disclosure of AI use in learning, research, and publication

This article addresses the following question: How should authors disclose the use of Artificial Intelligence (AI) in their research, as well as in the crafting of any manuscripts based upon their research so that readers, reviewers, and editors can clearly see how and where AI shaped a scholarly contribution? The question was discussed in a presentation at the 2025 NISO Plus conference in which the current state of AI disclosure was examined. The ultimate recommendation that emerged is that the Artificial Intelligence Disclosure (AID) Framework could serve as the basis of an international standard for AI disclosure because the Framework supports standardized, consistent, and transparent reporting of AI use across the full arc of learning, research, and publication. It is designed to complement, not replace, the conventional citation of direct AI outputs, and it situates AI assistance in context to make process-level work visible.

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Artificial intelligence and transparency: Toward a framework for disclosure of AI use in learning, research, and publication

OpenAlex · Artificial Intelligence in Healthcare and Education · 2026

Abstract

This article addresses the following question: How should authors disclose the use of Artificial Intelligence (AI) in their research, as well as in the crafting of any manuscripts based upon their research so that readers, reviewers, and editors can clearly see how and where AI shaped a scholarly contribution? The question was discussed in a presentation at the 2025 NISO Plus conference in which the current state of AI disclosure was examined. The ultimate recommendation that emerged is that the Artificial Intelligence Disclosure (AID) Framework could serve as the basis of an international standard for AI disclosure because the Framework supports standardized, consistent, and transparent reporting of AI use across the full arc of learning, research, and publication. It is designed to complement, not replace, the conventional citation of direct AI outputs, and it situates AI assistance in context to make process-level work visible.

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