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Artificial Intelligence and Authorship Editor Policy: ChatGPT, Bard Bing AI, and beyond
Editorial   Open access   Peer reviewed

Artificial Intelligence and Authorship Editor Policy: ChatGPT, Bard Bing AI, and beyond

Joseph Crawford, Michael Cowling, Sally Ashton-Hay, Jo-Anne Kelder, Rebekkah Middleton and Gail S Wilson
2023
DOI: https://doi.org/10.53761/1.20.5.01
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Artificial Intelligence and Authorship Editor Policy: ChatGPT, Bard Bing AI, and beyondView
Published (Version of record)Free to Read,  Open

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Abstract

Large language model AI contributions editorial policy AI-informed research academic integrity ethical research
Artificial intelligence and large-language model chatbots have generated significant attention in higher education, and in research practice. Whether ChatGPT, Bard, Jasper Chat, Socratic, Bing AI, DialoGPT, or something else, these are all shaping how education and research occur. In this Editorial, we offer five editorial principles to guide decision-making for editors, which will also become policy for the Journal of University Teaching and Learning Practice. First, we articulate that non-human authorship does not constitute authorship. Second, artificial intelligence should be leveraged to support authors. Third, artificial intelligence can offer useful feedback and pre-review. Fourth, transparency of artificial intelligence usage is an expectation. And fifth, the use of AI in research design, conduct, and dissemination must comply with established ethical principles. In these five principles, we articulate a position of optimism for the new forms of knowledge and research we might garner. We see AI as a mechanism that may augment our current practices but will not likely replace all of them. However, we do issue caution to the limitations of large language models including possible proliferation of poor-quality research, Stochastic Parroting, and data hallucinations. As with all research, authors should be comfortably familiar with the underlying methods being used to generate data and should ensure a clear understanding of the AI tools being used prior to deployment for research.

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