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.)
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