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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsYes—papers suspected of being fabricated with ChatGPT have appeared as ordinary Google Scholar results. A 2024 study identified 139 such papers, but its phrase-based search was a targeted sample, not a measure of how many Google Scholar records are AI-generated. The practical takeaway is to treat a Scholar result as a lead to investigate, not proof that a paper is genuine or reliable.
What the 2024 study found
Jutta Haider, Kristofer Söderström, Björn Ekström, and Malte Rödl reported their findings in the Harvard Kennedy School Misinformation Review in 2024. They searched for papers containing one or both of two recurring ChatGPT phrases: “as of my last knowledge update” and “I don’t have access to real-time data.”
The search retrieved 227 papers. After excluding 88 papers that represented legitimate or declared uses of GPT, the researchers classified 139 as undeclared or fraudulent. Their classification involved collaborative coding and cross-checking; it should not be read as a direct count of every AI-written paper in Google Scholar.
| Type of result | Number in the study sample |
|---|---|
| Papers in indexed journals | 19 |
| Papers in non-indexed journals | 89 |
| Student papers | 19 |
| Working papers | 12 |
Health and environment topics accounted for 47 papers, or 34% of the 139-paper sample. The researchers described the set as a magnifying glass on a wider problem, not an estimate of the proportion of all Scholar records that are fabricated. Searching for telltale phrases can miss AI-generated writing that uses different wording, and the phrases alone cannot establish misconduct in every case.
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Why can a fake or questionable paper appear in Google Scholar?
Google Scholar is designed for broad discovery. It brings together material from different kinds of sources, including journal articles, preprints, reports, student papers, and other gray literature. That breadth is useful when searching across scholarly material, but a result appearing there does not certify that its venue uses rigorous peer review or that the paper itself is trustworthy.
Haider and colleagues also found that copies of identified papers appeared across multiple locations, including repositories, ResearchGate, ORCiD, EasyChair, IEEE, Frontiers, and social media. When a paper has been copied or deposited in several places, withdrawing it from one venue may not remove every accessible version.
How can fabricated papers affect research and public decisions?
A fabricated study can enter a literature review, be cited as support for a claim, or be used to make a weak or incorrect argument look backed by research. The authors describe this broader practice as “evidence hacking”: exploiting the presence and appearance of research evidence to influence how a claim is received. If a paper is repeated across platforms or cited by later work, it can become harder for readers to distinguish a credible finding from a false one.
The concern is not limited to scholarly debate. As Björn Ekström put it in a University of Borås summary of the work, “The risk of what we call ‘evidence hacking’ increases significantly when AI-generated research is spread in search engines. This can have tangible consequences as incorrect results can seep further into society and possibly also into more and more domains.” Haider emphasized the information-literacy dimension: “If we cannot trust that the research we read is genuine, we risk making decisions based on incorrect information. But as much as this is a question of scientific misconduct, it is a question of media and information literacy.”
What a 2025 study adds about citations and metrics
A separate study published in Scientific Reports in 2025 analyzed more than 1.6 million Google Scholar profiles and examined how weakly moderated sources could enable citation planting with AI-generated papers. In a fictional demonstration profile, planted papers produced 380 citations and an h-index of 19 on Google Scholar.
Those figures came from the study’s fictional example, not from a claim that a real researcher gained those citations or that Scholar metrics are generally invalid. The demonstration shows why citation totals and profile metrics should be interpreted with attention to how the underlying papers entered the scholarly record.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to check a Google Scholar result before relying on it
Use the result to locate the paper and inspect its provenance—the path by which it was published, deposited, and cited. These checks do not prove authorship on their own, but they help reveal when a record needs closer scrutiny.
- Open the original venue. Identify the journal, conference, repository, or institution responsible for the version you found. A result hosted by a repository or listed in Scholar is not, by itself, evidence of peer review.
- Check peer-review status. Look for the venue’s stated review process and whether the item is a journal article, preprint, working paper, student paper, or another document type. Do not treat these categories as interchangeable.
- Verify the authors and affiliations. Check whether the named authors can be connected to the institutions or research areas listed, and whether the paper is represented consistently on credible institutional or professional pages.
- Compare versions and copies. Search distinctive phrases from the title or abstract to see whether the same text appears in several repositories or platforms. Multiple copies may reflect duplication rather than independent confirmation.
- Inspect references and claims. Follow a few important citations to confirm they exist and support the claims attributed to them. Be alert to references that are incomplete, irrelevant, or difficult to verify.
- Look for corrections or retractions. Check the venue’s notices and the paper’s record for corrections, expressions of concern, or retraction information before citing or applying its conclusions.
These checks are especially important when a paper is being used to support a consequential health, environmental, or policy claim. A suspicious phrase can be a reason to investigate, but it is not a standalone test for AI authorship; stronger judgment comes from checking the paper’s venue, authorship, references, and publication history together.
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