Authorship Statement for Generative Artificial Intelligence: Assuring Trust and Accountability.
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| 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 194630171 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Authorship Statement for Generative Artificial Intelligence: Assuring Trust and Accountability. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Crawford%2C+Joseph%22">Crawford, Joseph</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Purvis%2C+Alison+J%2E%22">Purvis, Alison J.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Grieve%2C+Averil%22">Grieve, Averil</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Taylor%2C+Louise%22">Taylor, Louise</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+University+Teaching+%26+Learning+Practice%22">Journal of University Teaching & Learning Practice</searchLink>. 2026 1st Quarter, Vol. 23 Issue 1, p1-13. 13p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Authorship%22">Authorship</searchLink><br />*<searchLink fieldCode="DE" term="%22Scholarly+publishing%22">Scholarly publishing</searchLink><br />*<searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Scholarly+peer+review%22">Scholarly peer review</searchLink><br /><searchLink fieldCode="DE" term="%22Honesty%22">Honesty</searchLink><br /><searchLink fieldCode="DE" term="%22Data+protection%22">Data protection</searchLink><br /><searchLink fieldCode="DE" term="%22Ethics%22">Ethics</searchLink><br /><searchLink fieldCode="DE" term="%22Government+accountability%22">Government accountability</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>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.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=194630171 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.53761/v16abt43 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Authorship Type: general – SubjectFull: Scholarly publishing Type: general – SubjectFull: Generative artificial intelligence Type: general – SubjectFull: Scholarly peer review Type: general – SubjectFull: Honesty Type: general – SubjectFull: Data protection Type: general – SubjectFull: Ethics Type: general – SubjectFull: Government accountability Type: general Titles: – TitleFull: Authorship Statement for Generative Artificial Intelligence: Assuring Trust and Accountability. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Crawford, Joseph – PersonEntity: Name: NameFull: Purvis, Alison J. – PersonEntity: Name: NameFull: Grieve, Averil – PersonEntity: Name: NameFull: Taylor, Louise IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2026 1st Quarter Type: published Y: 2026 Identifiers: – Type: issn-print Value: 14499789 Numbering: – Type: volume Value: 23 – Type: issue Value: 1 Titles: – TitleFull: Journal of University Teaching & Learning Practice Type: main |
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