Authorship Statement for Generative Artificial Intelligence: Assuring Trust and Accountability.

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Bibliographic Details
Title:	Authorship Statement for Generative Artificial Intelligence: Assuring Trust and Accountability.
Authors:	Crawford, Joseph¹, Purvis, Alison J.², Grieve, Averil³, Taylor, Louise⁴
Source:	Journal of University Teaching & Learning Practice. 2026 1st Quarter, Vol. 23 Issue 1, p1-13. 13p.
Subject Terms:	Authorship, Scholarly publishing, Generative artificial intelligence, Scholarly peer review, Honesty, Data protection, Ethics, Government accountability
Abstract:	Generative artificial intelligence (GenAI) has accelerated the production of academic text, images, and analytic outputs, while simultaneously destabilising long-standing cues used to infer human authorship and accountability. As a result, manuscripts increasingly arrive with unclear boundaries between human contribution, tool-assisted editing, and tool-generated content, and these distinctions are rarely made explicit. This is a reader and reviewer issue and a governance challenge for journals seeking consistent peer review and editorial decision-making. Since our last policy in 2023, there have been new practice evolutions: GenAI's entangled and multimodal workflow integration, partial convergence in publishing standards, heightened confidentiality and data governance risks, the post-plagiarism imperative to prioritise transparency over detection, and the increasing conceptual complexity of defining what constitutes 'AI use'. We set out six commitments covering: specific disclosure requirements, prohibition of GenAI generating the manuscript's substantive scholarly contribution, human centrality and confidentiality in peer review, conditions for transparent use of synthetic media, mandatory reflexivity when GenAI is used in methods or analysis, and the non-transferability of accountability away from named authors. This position aims to preserve trust by making responsibility legible again. [ABSTRACT FROM AUTHOR]
	Copyright of Journal of University Teaching & Learning Practice is the property of Open Access Publishing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Education Research Complete

Description
Abstract:	Generative artificial intelligence (GenAI) has accelerated the production of academic text, images, and analytic outputs, while simultaneously destabilising long-standing cues used to infer human authorship and accountability. As a result, manuscripts increasingly arrive with unclear boundaries between human contribution, tool-assisted editing, and tool-generated content, and these distinctions are rarely made explicit. This is a reader and reviewer issue and a governance challenge for journals seeking consistent peer review and editorial decision-making. Since our last policy in 2023, there have been new practice evolutions: GenAI's entangled and multimodal workflow integration, partial convergence in publishing standards, heightened confidentiality and data governance risks, the post-plagiarism imperative to prioritise transparency over detection, and the increasing conceptual complexity of defining what constitutes 'AI use'. We set out six commitments covering: specific disclosure requirements, prohibition of GenAI generating the manuscript's substantive scholarly contribution, human centrality and confidentiality in peer review, conditions for transparent use of synthetic media, mandatory reflexivity when GenAI is used in methods or analysis, and the non-transferability of accountability away from named authors. This position aims to preserve trust by making responsibility legible again. [ABSTRACT FROM AUTHOR]
ISSN:	14499789
DOI:	10.53761/v16abt43