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    In my research recently published in an open access journal, I used a popular language model, GPT-4 by OpenAI, to create simple summaries of scientific papers. These summaries generated by artificial intelligence (AI) used simpler language and more common words, like “job” instead of “occupation”, than summaries written by the researchers who had done the work.

    In one experiment, I found that readers of the AI-generated summaries had a better understanding of the science than readers of the human-written summaries. A second experiment investigated what effects the simpler summaries might have on people’s perceptions of the scientists who performed the research. In this experiment, participants rated the scientists whose work was described in the simpler texts as more credible than the scientists whose work was described in the more complex texts.

    Have you ever read about a scientific discovery and felt like it was written in a foreign language? New scientific information is probably hard to understand — especially if you try to read a science article in a research journal. In an era where understanding science is crucial for informed decision- -making, the abilities to comprehend and communicate complex ideas are more important than ever. Trust in science has been declining for years, and one contributing factor may be the challenge of understanding scientific jargon.

    As AI continues to evolve, its role in science communication may expand, especially if using generative AI becomes more commonplace. Simple science descriptions are preferable to and more beneficial than complex ones, and AI tools can help. But scientists could also achieve the same goals by working harder to minimize jargon and communicate clearly — no AI necessary.

(David Markowitz. https://theconversation.com, 30.10.2024. Adaptado.)