I Used AI to Fake a Scientific Paper. Here’s What Happened

Science has a spam problem. And the spam filter is getting clogged up. According to all reports, academic publishing is drowning in what has come to be called AI slop: low-quality, mass-produced content generated with AI. By 2025, an estimated 57 percent of all published academic articles exhibited detectable evidence of large language model (LLM) influence, a massive jump from 12 percent in 2023. What’s more, over 12 percent of Nature Communications reviews were found to be completely AI-generated in 2025. Given that for every 40 papers showing statistical evidence of AI usage in 2025, only one formally disclosed it , these figures likely grossly underestimate LLM involvement today. Of course, evidence of AI use doesn’t necessarily mean a paper is slop. But some estimates suggest that hundreds of thousands of fake, often AI-generated, publications are now produced annually. “It’s not just the [scientific] record under threat, it’s science as a whole,” warns Amanda Sulicz , IEEE’s research-integrity manager. “AI slop is completely eroding trust in science and every scientific process that we’ve worked for centuries to create.” Yet statistics and statements claiming that the problem is big—perhaps even existential for science—don’t really get the message across. Just how easy is it to use AI to create a convincing but completely fake paper? And without adding even more to the burden on stretched peer reviewers, who do this work on a volunteer basis, do publishers have the tools to keep AI slop out of the scientific record? Borrowing from the idea of “vibe coding,” in which p

AI-generated fake papers erode trust in science

The proliferation of AI-generated low-quality or fake scientific papers severely undermines trust in the scientific record and peer review processes. This widespread AI misuse threatens the integrity of scientific progress, causing material harm to the reliability of published research. The evidence is strong and specific, showing rapid growth and underreporting of AI involvement in publications.

The article cites specific statistics on rising AI-generated content and undisclosed use in academic publishing, plus a demonstrated experiment showing ease of faking a paper. This points to a real erosion of trust in scientific processes, though some figures are estimates. Negative implication is well supported but not fully primary data.

The article presents statistics indicating a rapid increase in AI-generated academic content, with a significant percentage of articles showing LLM influence and many being entirely AI-generated without disclosure. It warns of an existential threat to science and the erosion of trust in scientific processes, suggesting a profound negative impact on human knowledge and progress. The author's experiment to generate a fake paper highlights the ease of this process.

The text describes a significant and growing problem of AI-generated 'slop' in academic publishing. This undermines trust in science. The author's experiment shows how easy it is to create convincing but fake papers.

SOURCE     IEEE Spectrum: AI (tier 1)
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DOCUMENT   2e50a2fe-24ea-4b78-87fd-969f589bad6b
           https://spectrum.ieee.org/ai-generated-scientific-papers
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EVIDENCE   2 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      -71  (Strongly adverse)
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CONFIDENCE 67%