Summary: AI-generated summaries make scientific studies more accessible and improve public trust in scientists. Using GPT-4, researchers created simplified summaries that were easier to read and understand than human-written ones.
Participants rated scientists whose work was described in simpler terms as more credible and trustworthy. While promising, using AI in science communication raises ethical concerns about accuracy, transparency, and potential oversimplification.
Key Facts:
- AI-generated summaries improve public comprehension of complex studies.
- Simpler language boosts trust in scientists and their credibility.
- Ethical concerns include the loss of nuance and need for transparency in AI use.
Source: Michigan State University
Have you ever read about a scientific discovery and felt like it was written in a foreign language?
If you’re like most Americans, new scientific information can prove challenging to understand — especially if you try to tackle a science article in a research journal.
In an era when scientific literacy is crucial for informed decision-making, the abilities to communicate and comprehend complex content 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.
New research from David Markowitz, associate professor of communication at Michigan State University, points to a potential solution: using artificial intelligence, or AI, to simplify science communication.
His work demonstrates that AI-generated summaries may help restore trust in scientists and, in turn, encourage greater public engagement with scientific issues — just by making scientific content more approachable.
The question of trust is particularly important, as people often rely on science to inform decisions in their daily lives, from choosing what foods to eat to making critical heath care choices.
Responses are excerpts from an article originally published in The Conversation.
How did simpler, AI-generated summaries affect the general public’s comprehension of scientific studies?
Artificial intelligence can generate summaries of scientific papers that make complex information more understandable for the public compared with human-written summaries, according to Markowitz’s recent study, which was published in PNAS Nexus.
AI-generated summaries not only improved public comprehension of science but also enhanced how people perceived scientists.
Markowitz used a popular large language model, GPT-4 by OpenAI, to create simple summaries of scientific papers; this kind of text is often called a significance statement.
The AI-generated summaries used simpler language — they were easier to read according to a readability index and used more common words, like “job” instead of “occupation” — than summaries written by the researchers who had done the work.
In one experiment, he found that readers of the AI-generated statements had a better understanding of the science, and they provided more detailed, accurate summaries of the content than readers of the human-written statements.
How did simpler, AI-generated summaries affect the general public’s perception of scientists?
In another experiment, participants rated the scientists whose work was described in simple terms as more credible and trustworthy than the scientists whose work was described in more complex terms.
In both experiments, participants did not know who wrote each summary. The simpler texts were always AI-generated, and the complex texts were always human-generated. When I asked participants who they believed wrote each summary, they ironically thought the more complex ones were written by AI and simpler ones were written by humans.
What do we still need to learn about AI and science communication?
As AI continues to evolve, its role in science communication may expand, especially if using generative AI becomes more commonplace or sanctioned by journals. Indeed, the academic publishing field is still establishing norms regarding the use of AI. By simplifying scientific writing, AI could contribute to more engagement with complex issues.
While the benefits of AI-generated science communication are perhaps clear, ethical considerations must also be considered. There is some risk that relying on AI to simplify scientific content may remove nuance, potentially leading to misunderstandings or oversimplification.
There’s always the chance of errors, too, if no one pays close attention. Additionally, transparency is critical. Readers should be informed when AI is used to generate summaries to avoid potential biases.
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.
About this AI and science communication research news
Author: Alex Tekip
Source: Michigan State University
Contact: Alex Tekip – Michigan State University
Image: The image is credited to Neuroscience News
Original Research: Open access.
“From complexity to clarity: How AI enhances perceptions of scientists and the public’s understanding of science” by David Markowitz et al. PNAS Nexus
Abstract
From complexity to clarity: How AI enhances perceptions of scientists and the public’s understanding of science
This article evaluated the effectiveness of using generative AI to simplify science communication and enhance the public’s understanding of science.
By comparing lay summaries of journal articles from PNAS, yoked to those generated by AI, this work first assessed linguistic simplicity differences across such summaries and public perceptions in follow-up experiments.
Specifically, study 1a analyzed simplicity features of PNAS abstracts (scientific summaries) and significance statements (lay summaries), observing that lay summaries were indeed linguistically simpler, but effect size differences were small.
Study 1b used a large language model, GPT-4, to create significance statements based on paper abstracts and this more than doubled the average effect size without fine-tuning.
Study 2 experimentally demonstrated that simply-written generative pre-trained transformer (GPT) summaries facilitated more favorable perceptions of scientists (they were perceived as more credible and trustworthy, but less intelligent) than more complexly written human PNAS summaries.
Crucially, study 3 experimentally demonstrated that participants comprehended scientific writing better after reading simple GPT summaries compared to complex PNAS summaries.
In their own words, participants also summarized scientific papers in a more detailed and concrete manner after reading GPT summaries compared to PNAS summaries of the same article.
AI has the potential to engage scientific communities and the public via a simple language heuristic, advocating for its integration into scientific dissemination for a more informed society.
This post was originally published on here
