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

My experiment with an IEEE journal revealed AI’s threats to scholarly publishing Benjamin Skuse set out to test whether AI-generated research could pass for the real thing. Science has a spamproblem. 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 estimated57 percent of all published academic articlesexhibited detectable evidence of large language model (LLM) influence, a massive jump from 12 percent in 2023. What’s more,over 12 percent ofNature Communicationsreviewswere 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,” warnsAmanda 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 programmers use natural-language prompts to generate code, I decided to “vibe-research” a made-up paper by prompting LLMs to generate hypotheses, code, and text with minimal human input. Then I askedJenny Mahoney, managing editor of the multidisciplinary and open-access journalIEEE Access,if we could war-game an attempt to sneak my AI-generated monstrosity past her journal’s defenses. She and IEEE kindly agreed.The experiment would become a two-part test. First, whether someone with no special AI expertise could use off-the-shelf AI tools to generate a convincing paper describing research that had never been performed, and do it well enough to fool a publisher’s defenses. Then I would raise the stakes: What happens when more advanced AI systems don’t just write the paper but conduct underlying research?Along the way, the experiment became a window into how AI automation is transforming scientific discovery and forcing stakeholders to rethink the entire academic publishing system.Round 1: Creating the ForgeryTo give me a chance at creating a convincing fake, I gave myself permission to prompt multiple times (within reason). But to make the fight fair, I couldn’t access the most advanced paid versions of LLMs—only freeChatGPT(at the time, GPT-5.3/5.2 Mini) and Gemini (3 Flash). Moreover, I could not directly edit any output, nor ask for outside help from expert AI-prompt engineers. This needed to be an amateur effort, with the paper generated as much as possible by the LLMs alone.Benjamin Skuse put AI to work on scientific research from his writing studio.Luigi AvantaggiatoI assumed that using fake author names and affiliations would immediately raise a red flag, and I didn’t want to drag the names of established academics into the experiment, so the best bet was to name myself as the sole author. That choice limited me to subjects in which I had some academic experience. So I chose my Ph.D. topic from nearly two decades ago: optical solitons, which are individual light pulses that propagate without any change to their shape.My first attempt—“Can you vibe-research me an original paper in optical solitons?”—left a lot to be desired. Though Gemini was happy to do the task (unlike ChatGPT, which was more forceful in not generatingsynthetic dataor studies), the result was underwhelming. And it highlighted a recurring problem during the process: Without any real research to work with, the output was laughably thin, with the LLMs often struggling to fill four pages. However, as I became more adept at prompting the models, they loosened up and became more characteristically loquacious.I broke the paper down into its constituent parts—abstract, introduction, methods, and so on—and tackled them one by one. If one LLM refused to go any further, switching to the other and asking what the next step would look like “hypothetically” would often do the trick. After a dozen or so prompts, I had a realistic-looking paper. A dozen or so more prompts generated and tidied up the figures and captions, produced references and citations, and converted the paper intoIEEE Access’s required style.At this point, I read the full paper carefully. To me, it looked pretty good. The science sounded convincing. I even had visions of returning to academia to conduct in real life the experiments that the LLMs had proposed.What’s more, I couldn’t see any obvious telltale “AI-isms” that would immediately be flagged. For example, the LLMs didn’t produce any tortured phrases—unusual paraphrases of standard technical terms, like “profound neural organization” replacing “deep neural network.”Other AI-isms were arguably present, involving stylistic tics like hedging language (“potentially, typically”), grouping things in threes (“The study provides a clear, concise, and actionable framework”), and inserting formulaic transitions (“In summary,…”). But as the previous sentence demonstrates, these tics are hard to spot because they are often used by unimaginative humans, too.Round 1: Attempt to PublishTwo Ways to Make a Paper With AIJorge Cuadel CalleI took two very different routes to creating a scientific paper with AI. First, I coaxed free, general-purposechatbotsthrough the process step by step; then I turned to FARS, an autonomous research system designed to do much of the scientific work itself.ROUND 1: CHATGPT + GEMINI1. Choose a research topicI ask the free chatbots to create an original paper on optical solitons, my former Ph.D. field.2. Break the paper into piecesThe initial results are thin, so I prompt separately for the abstract, introduction, methods, and other sections.3. Keep steering the LLMsWhen one model refuses or stalls, I switch models, rephrase prompts, and ask what the next step would look like “hypothetically.”4. Build and polish the paperAfter about two dozen prompts, the LLMs have generated the text, figures, captions, references, and citations and formatted the paper forIEEE Access.5. Put it to the testI send the fake paper toIEEE Access, where automated integrity checks flag fabricated and unverifiable references.→CAUGHTIEEE’s integrity checks flag fabricated and unverifiable references.ROUND 2: FARS1. Give FARS a research topicI enter an abbreviated version of the same optical-soliton topic. FARS generates a research proposal in 21 minutes.2. Authorize the experimentWith a click, I tell FARS to carry out the experiment described in its proposal.3. Let FARS do the researchFARS conducts the computational experiment and emails me the next day to say it’s complete.4. Tell FARS to write the paperWithout checking the results, I click “Write paper.” Two hours later, FARS produces a submission-ready article.5. Put it to the testI remove FARS’s disclosure, put my name on the paper, and send it toIEEE Access.→CAUGHTThe references are real, but IEEE flags the paper’s heavy reliance on non-peer-reviewed preprints.Handing the fake paper over toIEEEAccessleft me torn. After putting in a fair amount of effort into making the AI-generated forgery, would I be happy if it passed through the checkers, or saddened that the only remaining line of defense for publishers is the evermore-burdened peer-review community? And if it was spotted, would I be angry tha