Science is no longer a purely human endeavor. Machine minds are increasingly merging with human creativity to generate hypotheses, experiments, and ideas.
But with promise comes misuse. Like TikTok videos and AI-generated images splattered across the internet, AI slop has infiltrated science. Academic journals and conferences are being flooded with AI-generated content, and editors are drawing the line. As long as they disclose it, scientists can use chatbots to help write papers, as long as the underlying ideas are their own. Submitting a whole paper dreamed up by AI, with little human input, is another matter.
Publishers are turning to AI detectors to weed out slop. But detectors aren’t perfect. Most scrutinize turns of phrase, word choice, and other intricacies associated with machine-generated text. They can mislabel legitimate research because of how the paper is written, rather than on the merit or origin of its ideas. That’s especially problematic for scientists whose first language isn’t English and who rely on AI for heavy editing.
A team at the University of Maryland in College Park is now taking a different approach: Strip away the prose until only a skeleton of concepts remains. Their detector, called IdeaLens, learned to find patterns in how human and AI models organize ideas, build narratives, and construct arguments, all of which are integral to every scientific paper.
IdeaLens alone won’t stem the deluge of AI slop polluting scientific literature. But it joins a growing toolset that recognizes human contributions, even as the boundary between scientist- and AI-generated ideas grows blurry.
“Discerning the extent to which AI performs critical thinking—a task that, in our view, rightly belongs to human authors—is increasingly difficult,” wrote editors of The Journal of Clinical Psychiatry, who were not involved in the new work. But, “using AI to deliberately fabricate data or automate the interpretation of study findings crosses ethical lines and undermines scientific integrity.”
Fighting Fire With Fire
There’s no doubt that AI has already made waves in science.
Algorithms are tackling particle and space physics and deciphering biology. Drug companies are betting AI can accelerate drug discovery and fight cancer, Alzheimer’s, and other medical scourges that continue to haunt millions worldwide.
Amid controversy, OpenAI recently announced an AI-powered breakthrough in fluid dynamics—one of the longest-standing, toughest puzzles in mathematics. While welcomed by some, the advance, alongside a barrage of other results, has triggered an “existential crisis” within the field and sparked debate over who deserves credit.
Generative AI has boosted research productivity over 89 percent, according to a study in late 2025. But there are growing pains. One estimate suggests roughly 12.5 percent of biomedical articles show signs of substantial AI involvement. And even AI conferences have struggled with AI submissions: Some papers accepted to the prestigious 2025 NeurIPs conference were flagged as likely AI-generated and 100 or more hallucinated citations slipped past human reviewers.
Publishers are pushing back.
The popular preprint server arXiv has tightened submission rules for computer science reviews, requiring prior peer review to combat the flood of submissions partially fueled by AI.
Others are using detectors. AI writing has particular quirks in word choice and sentence structure. In a well-known example, models tend to overuse constructions like, “It’s not X, it’s Y.” One popular tool, Pangram, claims 99 percent detection accuracy and is now integrated into alphaXiv, an arXiv mirror site, to automatically flag potential machine-written manuscripts. Meanwhile, scientists are turning papers into AI agents, making it easier to reproduce findings and spot questionable research.
Instead of focusing on wording, IdeaLens asks if the underlying concepts are human or AI.
A New Detective
The tool’s creators previously found AI-written fiction differs structurally from human work. Stories generated by machines tend to be tidier, more predictable, and more on the nose about their message. Humans write sloppier but more colorful tales. Beyond surface-level word choice or tone, the team found differences embedded into the narrative itself. For example, human writers often wander into subplots, let readers infer themes, explore morally debatable choices, and leave ambiguous endings. These detours create rich, lived-in worlds that reflect the messiness of real life. AI prefers straightforward narratives and neatly tied loose ends.
These findings inspired the team to develop IdeaLens, which looks past prose and focuses on structural and conceptual patterns.
The researchers first used Pangram to label roughly a million texts, such as news articles, blog posts, and academic papers, as human- or AI-generated. A large language model then stripped away the original wording, leaving a detailed outline of the main points. Scientific papers, for instance, were distilled to background information, claims, evidence, and arguments. Another model, called a classifier, then learned to find patterns that distinguished human- from AI-generated texts in these skeletal rewrites.
Like many modern AI systems, IdeaLens is a “black box.” The team can’t fully explain how it lands on verdicts, although they did find human-derived outlines tend to contain more study-specific details, rather than generic points often used by AI that could apply to other studies.
Still, it worked.
When challenged with documents written by ChatGPT and other large language models, IdeaLens mistakenly flagged them as human work just one percent of the time. For texts lightly edited by AI, it correctly recognized human origins in about 95 percent of cases. Across evaluation datasets, it missed roughly 10 percent of AI-generated work.
That might seem like a big problem. But “we would much rather have that AI document slip through than humans wrongly being flagged as AI,” study author Rishanth Rajendhran told Nature. “Because of the reputational damage that these things carry, it is important that we are careful with what we do with the prediction.” Once flagged, human reviewers should jump in for a closer look at the questionable submission.
Like other detectors, IdeaLens has blind spots. The team didn’t individually verify the origins of every document in its training data. The tool was also never tested on genuinely hybrid articles, where humans and AI models co-develop the underlying concepts, which is an increasingly common scenario in science. As scientists turn to chatbots to refine hypotheses or spark new ones, intellectual ownership becomes even murkier.
For now, IdeaLens sees the world in black and white, assigning just two labels: human or AI. Future versions may introduce more nuance, such as assigning the estimated percentages of their contributions. You can try it here.
The team stresses the detector shouldn’t discourage scientists from embracing AI. Rather, the differences it found highlight the value of human ingenuity. Without a dose of human messiness and out-the-box thinking, AI slop risks reshaping not just scientific literature, but our understanding of the world too.
