Scientific literacy must keep pace with artificial intelligence

AI is making science easier to access but harder to trust. Scientific literacy must catch up, writes Giulia Stefenelli

Making informed decisions about our health, the environment, and other areas of public policy requires us to understand the underlying scientific evidence. Artificial Intelligence (AI) can help explain that evidence, but does the explanation accurately reflect the research, and is the research itself reliable? These questions are central to scientific literacy and become even more important as AI changes how scientific information is produced and communicated.

For decades, one of the biggest barriers to reading scientific literature was accessing it. Today, thanks largely to the expansion of open-access publishing and evolving funder mandates, far more literature is free to read and download. Figures from Dimensions, for example, suggest that around 61% of global journal articles, reviews and conference papers published in 2025 were open access. However, access alone does not guarantee understanding or trust.

Generative AI, such as ChatGPT and Claude for example, are helping to eliminate another barrier: comprehension. It can, for example, help researchers navigate literature, help patients request plain-language explanations of clinical papers, and help policymakers compare and understand findings outside their specialist fields. At the same time, however, it is creating a new imbalance. While scientific-looking content can now be produced quickly and at scale, verifying its data, references, methods and conclusions still requires time, expertise and access to the underlying evidence. Generation is becoming cheap, but verification is not.