by Dirk Lindebaum
Core to the mission of SRHE is the commitment to high-quality research and scholarship to advance understanding of higher education. This focus makes the conditions under which research is produced, including editorial and journal policies concerning the publication process, a topic of natural interest to readers of the SRHE blog.
The publication process, and how stakeholders interact in that process, however, is profoundly changing due to the widespread use of generative AI (hereafter AI). Consider the irony that authors can now offload their writing to AI; that reviewers can use AI to perform reviews on AI-produced text; and that editors can use AI to make decisions on papers written by AI, and grounding decisions in AI-produced reviews. It is machines chatting with machines, and machines reacting to machines, just without the informed doubt, the willingness of daring to know, the realization that we as social scientists always condition and are conditioned through our work and social practices that follow from our work.
Notwithstanding this development, the use of AI in the research process is routinely and boldly argued to be ‘inevitable’. What is less often understood is that this narrative likely accelerates an already existing crisis of credibility in some fields (eg in management research). A sobering look at the available empirical evidence (summarized here) underlines that the crisis is real.
First, empirical studies suggest that, in 2025, more than 140,000 non-existing references have infiltrated scientific records. Second, while the quality of scholarship is decreasing (as indicated through a measure of language complexity), the use of AI helps researchers to produce more papers and faster (with direct effects on individual careers). All this unfolds against an already disadvantageous baseline, where disruptive and innovate research occurs less often. Third, while many AI journal polices exhort scholars to be the ‘human-in-the-loop’ to check the accuracy of AI outputs, empirical studies suggest that AI biases (caused by human biases in the training data) transfer and magnify in humans through epistemic feedback loops in Human-AI interactions. Fourth, many authors neglect to disclose AI-use, the consequence being that many AI journal policies fail to ensure transparency or constrain AI adoption. Hence, many AI journal policies concerning the use of AI may attempt to ‘police the unpoliceable’, despite the well-intending motives behind such policies.
After years of editorial frontline experience, I believe two policy moves are necessary to counter these developments which, if unaddressed, likely compromise the integrity of science. On the one hand, qualitatively speaking, I propose a set of evaluative criteria concerning how to evaluate (as editors and reviewers), and prepare submissions (as authors), in ways that mobilise judgment to strengthen the voice of authors in terms of mastery of the literature, method, analysis, contribution and recommendations for practice. Developing editor and reviewer judgment to strengthen author voices, and for authors to force themselves to nurture said judgment to inform their own voices distinctly and uniquely, is a matter of paramount importance when the defining scarcity of our time is not access to information or even knowledge, but rather the capacity to develop and apply sound judgment in the presence of seemingly boundless AI slop. I treat judgment here as epistemic judgment concerning reflexive justifications for truth claims – to oneself and others under conditions of uncertainty – and as expressed through the authors’ voice.
This effort has to be earned. This means persisting with developing judgment over time, not despite the struggle involved, but because of it. This is exactly why this struggle matters for AI advocates. While some argue that one needs to be an expert to use the AI in research, this point is often based on decades of building expertise before the arrival of AI. Yet, current socialization practices are such that junior colleagues and PhD students may not find the conditions in which to develop expertise. This does and will negatively affect expertise levels now and in future. The result is that the conditions of re-building expertise shrink, and that means the possibility to recover lost expertise decreases. It seems to me a caution that needs to be put plainly – this should concern usall. However, these ‘qualitative’ criteria are unworkable without also creating the conditions in which editors and reviewers can manage the current deluge of journal submissions.
Therefore, the second, quantitative, proposal is to introduce a submission cap per authors per annum (as assessed by ORCID IDs), given record numbers of submissions to many journals across science subjects. This would provide necessary ‘breathing space’ for often overstretched (volunteering) editors and reviewers, while promoting equitable access to editorial resources, reduce congestion in the peer review pipeline, and discourage the use of the journal as a preliminary check on manuscript submission readiness. Lastly, a submission cap can also counter the creeping ‘junkification’ of science – the mounting volume of low-quality content that permeate academic publishing, which will only intensify in the age of Gen-AI.
Subject to amendments to suit the needs of a particular journal, the proposal would work like this: each author can submit three new submissions per year to a journal, but with a twist to encourage authors to improve the quality of first submission. For each invitation to revise and resubmit a paper, authors could submit one additional paper to a journal (3 + 1). And if they are lucky to get a second (or third) invitation to revise and resubmit a paper, then that number would be 3 + 2 etc. The expansion is author-specific; if submission A gets a revise and resubmit, every author listed on submission A receives +1 to their personal limit at this journal. This expansion would apply to new submissions, and not to invited revisions.
Some colleagues may have reservations about the proposal due to a perceived limit to their productivity. However, let’s appreciate that even if a prolific scholar manages to publish three articles in each of the best five journals in their fields per year, these 15 articles can hardly be said to be detrimental to the scholar’s career prospects.
A submission cap is consistent with scientific progress because it helps editors and reviewers regain vital space to write quality letters and reviews based on the proposed new set of evaluative criteria. Hence, it is key to consider the complementary nature of both proposals, with the benefit of enabling the conditions under which published works can be trusted (more) in the age of Gen-AI. Epistemic agency would flow back to journals and their editors, for academics would set the agenda (rather than big tech).
Rather than advocating contra AI in the research process, my stance here is pro developing human judgment. If developed and assessed well in the research process, the focus on judgment will render the debate around AI journal policies of secondary relevance. Why try to police the unpoliceable if investing in human judgment is one of the best ways to safeguard the integrity of science?
Dirk Lindebaum is Professor of Management & Organisation at the University of Bath, and advocate and owner of “AI-Free Scholarship” which is a registered (noncommercial) trademark that applies to his writing and teaching.
