The arrival of generative artificial intelligence in academic research has produced something more disorienting than a simple ethical dilemma. It has created a situation in which every participant in scholarly publishing is being asked to make judgements that the system itself has not yet learned how to make.
Authors are told to be transparent, reviewers to be vigilant, editors to protect integrity. But the result is not a new culture of clarity. It is a culture of suspicion.
I experience this problem first as an author. Like many researchers, I do not approach AI as either a miracle or a threat. I approach it as a tool, whose boundaries remain strangely unclear. It can help with phrasing, structure, summaries, coding, translation, visualisation, brainstorming and literature mapping. Yet each of these uses occupies a different ethical position. Asking AI to polish a paragraph is not the same as asking it to generate an argument. Using it to produce a chart is not the same as using it to interpret data. Asking it to suggest possible lines of enquiry is not the same as outsourcing the intellectual work of the article.
This is where the anxiety begins. At what point does assistance become authorship? If I use AI to test an interpretation, sharpen a research question or generate alternative ways of presenting data, is that part of my process or a dilution of my contribution? Academic writing has never been the pure expression of a solitary mind. We discuss ideas with colleagues, receive feedback from reviewers, work with copyeditors, attend workshops and use software for analysis. Yet AI unsettles this familiar ecology because it produces language and sometimes appears to produce thought. It can mimic the surface of scholarly reasoning while being indifferent to truth, context and accountability.
