AI makes science easy, but is it getting it right? Study warns LLMs are oversimplifying critical research
large language models
(LLMs), including ChatGPT, Llama, and DeepSeek, might be doing too good a job at being too simple—and not in a good way.
According to a study published in the journal Royal Society Open Science and reported by Live Science, researchers discovered that newer versions of these AI models are not only more likely to oversimplify complex information but may also distort critical scientific findings. Their attempts to be concise are sometimes so sweeping that they risk misinforming healthcare professionals, policymakers, and the general public.
From Summarizing to Misleading
Led by Uwe Peters, a postdoctoral researcher at the
University of Bonn
, the study evaluated over 4,900 summaries generated by ten of the most popular LLMs, including four versions of ChatGPT, three of Claude, two of Llama, and one of DeepSeek. These were compared against human-generated summaries of academic research.
The results were stark: chatbot-generated summaries were nearly five times more likely than human ones to overgeneralize the findings. And when prompted to prioritize accuracy over simplicity, the chatbots didn't get better—they got worse. In fact, they were twice as likely to produce misleading summaries when specifically asked to be precise.
'Generalization can seem benign, or even helpful, until you realize it's changed the meaning of the original research,' Peters explained in an email to Live Science. What's more concerning is that the problem appears to be growing. The newer the model, the greater the risk of confidently delivered—but subtly incorrect—information.
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When a Safe Study Becomes a Medical Directive
In one striking example from the study, DeepSeek transformed a cautious phrase; 'was safe and could be performed successfully', into a bold and unqualified medical recommendation: 'is a safe and effective treatment option.' Another summary by Llama eliminated crucial qualifiers around the dosage and frequency of a diabetes drug, potentially leading to dangerous misinterpretations if used in real-world medical settings.
Max Rollwage, vice president of AI and research at Limbic, a clinical mental health AI firm, warned that 'biases can also take more subtle forms, like the quiet inflation of a claim's scope.' He added that AI summaries are already integrated into healthcare workflows, making accuracy all the more critical.
Why Are LLMs Getting This So Wrong?
Part of the issue stems from how LLMs are trained. Patricia Thaine, co-founder and CEO of Private AI, points out that many models learn from simplified science journalism rather than from peer-reviewed academic papers. This means they inherit and replicate those oversimplifications especially when tasked with summarizing already simplified content.
Even more critically, these models are often deployed across specialized domains like medicine and science without any expert supervision. 'That's a fundamental misuse of the technology,' Thaine told Live Science, emphasizing that task-specific training and oversight are essential to prevent real-world harm.
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iStock
Part of the issue stems from how LLMs are trained. Patricia Thaine, co-founder and CEO of Private AI, points out that many models learn from simplified science journalism rather than from peer-reviewed academic papers. (Image: iStock)
The Bigger Problem with AI and Science
Peters likens the issue to using a faulty photocopier each version of a copy loses a little more detail until what's left barely resembles the original. LLMs process information through complex computational layers, often trimming the nuanced limitations and context that are vital in scientific literature.
Earlier versions of these models were more likely to refuse to answer difficult questions. Ironically, as newer models have become more capable and 'instructable,' they've also become more confidently wrong.
'As their usage continues to grow, this poses a real risk of large-scale misinterpretation of science at a moment when public trust and scientific literacy are already under pressure,' Peters cautioned.
Guardrails, Not Guesswork
While the study's authors acknowledge some limitations, including the need to expand testing to non-English texts and different types of scientific claims they insist the findings should be a wake-up call. Developers need to create workflow safeguards that flag oversimplifications and prevent incorrect summaries from being mistaken for vetted, expert-approved conclusions.
In the end, the takeaway is clear: as impressive as AI chatbots may seem, their summaries are not infallible, and when it comes to science and medicine, there's little room for error masked as simplicity.
Because in the world of AI-generated science, a few extra words, or missing ones, can mean the difference between informed progress and dangerous misinformation.
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You remembered roads, lanes and routes; if you did not, you consulted a paper map or asked someone. Today, Google and other map apps do that work for us. Facebook reminds us of people's birthdays; email answers suggest themselves, sparing us of even that little effort of thinking. When autonomous cars arrive, will we even remember how to drive or just loll around in our seats as it takes us to our destination? Jonathan Haidt, in his 'The Anxious Generation,' points out how smartphones radically reshaped childhood. Unstructured outdoor play gave way to scrolling, and social bonds turned into notifications. Teen anxiety, loneliness, and attention deficits all surged. From calculators diminishing our mental arithmetic, to GPS weakening our spatial memory, every tool we invent alters us — subtly or drastically. 'Do we shape our tools, or do our tools shape us?' is a quote commonly misattributed to Marshall McLuhan but this question is hauntingly relevant in the age of AI. 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We definitely need some guardrails and precautions, and we need to start building them now. I believe that we should teach ourselves and our children to: Ask the right questions: As answers become commodities, asking the right questions will be the differentiator. We need to relook at our education system and pedagogy and bring back this unique human skill of curiosity. Intelligence is not just about answers. It is about the courage to think, to doubt, and to create Invert classwork and homework: Reserve classroom time for 'brain-only' activities like journaling, debates, and mental maths. Homework can be about using AI tools to learn what will be discussed in class the next day. AI usage codes: Just as schools restrict smartphone use, they should set clear boundaries for when and how AI can be used. Teacher-AI synergy: Train educators to use AI as a co-teacher, and not a crutch. Think of AI as Augmented Intelligence, not an alternative one. Above all, make everyone AI literate: Much like reading, writing, and arithmetic were foundational in the digital age, knowing how to use AI wisely is the new essential skill of our time. AI literacy is more than just knowing prompts. It means understanding when to use AI, and when not to; how to verify AI output for accuracy, bias, and logic; how to collaborate with AI without losing your own voice, and how to maintain cognitive and ethical agency in the age of intelligent machines. Just as we once taught 'reading, writing, adding, multiplying,' we must now teach 'thinking, prompting, questioning, verifying.' History shows that humans adapt. The printing press did not destroy memory; calculators did not end arithmetic; smartphones did not abolish communication. We evolved with them—sometimes clumsily, but always creatively. Today, with AI, the challenge is deeper because it imitates human cognition. In fact, as AI challenges us with higher levels of creativity and cognition, human intelligence and connection will become even more prized. Take chess: a computer defeated Gary Kasparov in chess back in 1997; since then, a computer or AI can defeat any chess champion hundred times out of hundred. But human 'brains-only' chess has become much more popular now, as millions follow D Gukesh's encounters with Magnus Carlsen. So, if we cultivate AI literacy and have the right guardrails in place; if we teach ourselves and our children to think with AI but not through it, we can come out wiser, not weaker. Facebook Twitter Linkedin Email Disclaimer Views expressed above are the author's own.
