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When AI Invents Scholarship: A Whistleblowing Paper, Fake References, and a System Under Strain
The first warning did not come from a journal, a reviewer, or an editorial board. It came from an independent researcher following a trail that was never meant to exist.
While investigating a whistleblowing case in Ethiopia, Finland-based researcher Erja Moore encountered a paper whose reference list immediately raised red flags. Curious rather than accusatory, she began tracing the cited literature. What she found was not poor scholarship or careless citation, but something far more troubling: a bibliography that appeared academically sound yet collapsed under even minimal scrutiny.
As Moore worked through the references, a clear pattern emerged. The paper cited studies that could not be found anywhere, including:
- References with no trace on Google Scholar
- Article titles that had never been published
- Citations pointing to real journals but with incorrect volumes, issues, authors, or page numbers
- Entirely fabricated titles assembled from plausible-sounding academic language
After systematically cross-checking the sources, Moore concluded that nearly two-thirds of the paper’s citations were fabricated.
The authors later acknowledged that they had used ChatGPT to generate the references, a disclosure that might have sounded extraordinary two years ago, but now reads as an increasingly familiar footnote in scholarly publishing.
The paper appeared in the Journal of Academic Ethics, published by Springer Nature.
Even more striking: eleven of the fabricated citations pointed to Springer Nature’s own Journal of Business Ethics.
The irony is difficult to overlook. A study on whistleblowing, a topic grounded in transparency and accountability, published in an ethics journal, relied on references generated by an AI system known to fabricate sources with confidence and fluency, but without accountability. Yet the deeper concern is not the symbolism. It is the question this case forces the entire industry to confront:
“How did this pass peer review?”
This is not an indictment of a single publisher or journal. It exposes a structural vulnerability that runs across the scholarly publishing ecosystem.
Traditional peer review operates on a fundamental assumption: that authors are citing real, existing scholarship. Reviewers assess arguments, methods, and conclusions; they do not routinely verify every reference, search every article title, or click every DOI. Editors neither expect nor have the capacity to demand that level of scrutiny.
AI disrupts that assumption.
Large language models can now generate references that look legitimate, author names that plausibly belong in the field, journals that genuinely exist, and publication details that appear realistic. Yet none of it is real. The result is an academic uncanny valley: citations convincing enough to pass casual inspection, but false enough to erode trust once exposed.
Moore captured the unease succinctly:
“We can’t really trust scientific articles anymore.”
While that statement may overreach, it reflects a growing anxiety shared quietly across editorial offices and research communities.
It is tempting to place responsibility solely on AI tools or on the authors who misused them. The authors’ actions clearly represent a breach of scholarly responsibility. But focusing only on individual misconduct obscures the broader context in which such failures occur.
AI hallucinations thrive in an academic environment shaped by structural pressures: the demand to publish quickly, the prioritization of quantity and indexation over rigor, overburdened reviewers, and editors managing ever-increasing submission volumes. AI did not create these incentives; it merely amplified the shortcuts they reward. In a system already stretched thin, efficiency can quietly replace verification, and often, no one checks.
Springer Nature has confirmed that it is investigating the case and exploring ways to identify AI-generated or hallucinated references. Other publishers have introduced reference-checking tools and updated AI-use policies. Yet detection remains difficult.
AI-generated citations are rarely obviously fake. They often involve real authors paired with fabricated titles, structurally valid but nonexistent DOIs, genuine journals with incorrect metadata, or real articles cited inaccurately. Automated tools struggle to detect these inconsistencies at scale, while human reviewers lack the time to verify every reference. This is not merely a technical gap; it is a systemic one.
The incident is alarming, but it may also serve as a necessary wake-up call. Practical measures could help narrow the widening trust gap, including mandatory source verification when AI tools are used, randomized reference audits to introduce meaningful deterrence, clearer and enforceable AI policies, and better reviewer support without shifting responsibility entirely onto automation. Ultimately, however, reforming incentives remains essential. As long as speed and indexation dominate research evaluation, technological shortcuts will continue to flourish.
Trust is the currency of scholarly publishing. Cases like this resonate because they expose how fragile that trust has become. When fabricated references appear in an ethics journal, confidence erodes not only in a single paper but in the system that allowed it through.
The encouraging sign is that the community is beginning to respond, questioning practices, revisiting policies, and confronting uncomfortable realities. AI is not going away, nor is the pressure to publish. But with transparent action, realistic safeguards, and a renewed commitment to integrity, the system can adapt. The alternative is far more damaging: a future in which readers must ask whether the reference list in front of them exists at all.
Keywords
Artificial intelligence Academic integrity Fake references AI hallucinations Scholarly publishing Peer review Research ethics Whistleblowing Reference fabrication Editorial oversight Publication pressure Trust in science
Maryam Sayab is the Director of Communications at the Asian Council of Science Editors (ACSE) and Co-Chair of Peer Review Week. With a background rooted in research integrity and publication ethics, she actively works to advance regional conversations around responsible peer review, transparent editorial practices, and inclusive open science. Maryam is dedicated to building bridges between global publishing standards and the practical realities faced by researchers and editors, especially across Asia and the Arab world. She also supports initiatives that strengthen community-driven collaboration, ethical scholarship, and the sustainable development of research ecosystems.
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The views and opinions expressed in this article are those of the author(s) and do not necessarily reflect the official policy or position of their affiliated institutions, the Asian Council of Science Editors (ACSE), or the Editor’s Café editorial team.
