Authorship Statement for Generative Artificial Intelligence: Assuring Trust and Accountability

Abstract

Generative artificial intelligence (GenAI) has accelerated the production of scholarly text, images, and analytic outputs, while simultaneously destabilising long-standing cues used to infer human authorship and scholarly 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 creates a veracity problem for readers and reviewers, uneven risk for authors, and governance challenges for journals seeking consistent peer review and editorial decision-making. This note articulates an updated and enforceable authorship position for the Journal of University Teaching and Learning Practice (JUTLP), responding to five evolutions since our 2023 stance. These new evolutions since 2023 include: GenAI’s entangled and multimodal integration into scholarly workflows, partial convergence in publishing standards, heightened confidentiality and data governance risks, the post-plagiarism imperative to prioritise transparency over detection, and the increasing 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 in the scholarly record by making responsibility legible again.