Could Science Benefit From AI-mediated Communication? This Study Says it Could

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(1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824.

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:::tip Editor's note: This is the final part of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. You can re-read the rest of the paper via the table of links below.

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• Abstract
• The Benefits of Simple Writing
• The Current Work
• Study 1a: Method
• Study 1a: Results
• Study 1b: Method
• Study 1b: Results
• Study 2: Method
• Study 2: Results
• General Discussion, Acknowledgements, and References

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The current work explored the potential of generative AI to simplify scientific communication, enhance public trust in scientists, and increase engagement in the understanding of science. The evidence suggested that while lay summaries from a top general science journal, PNAS, were linguistically simpler than scientific summaries, the degree of difference between these texts could be enlarged and improved. Generative AI assisted in making scientific texts simpler and more approachable compared to the human-written versions of such summaries. Therefore, this paper is notable given current challenges of scientific literacy and the disconnect between scientific communities and the public — AI is indeed better at communicating like a human (or the intentions of writing simply) than humans (42, 48). As prior work suggests, decreasing trust in scientists and scientific institutions, exacerbated by complex communication barriers, call for inventive solutions that are scalable and relatively inexpensive. Those that are offered here, particularly through generative AI, represent one potential pathway toward simpler, more approachable, and improved science communication.

\ These data build on a body of existing fluency research and provide empirical support for the hypothesis that linguistic simplicity, facilitated by AI, can significantly influence public perceptions of scientists’ credibility, trustworthiness, and intelligence. Generative AI, specifically large language models like GPT-4, can produce scientific summaries that are not only simpler, but also more accessible to lay audiences compared to those written by human experts. These results align with a broader scientific narrative (and interest) that advocates for clearer and more direct communication strategies in science dissemination (49).

\ The implications of this paper are twofold. First, the results suggest that leveraging AI in scientific communication can bridge scientific communities and the general public. This could be particularly beneficial in a time where science is increasingly central to everyday decision-making but is also viewed with skepticism or deemed inaccessible by non-experts. Second, the increased readability and approachability of AI-generated texts might contribute to a higher engagement with scientific content, thereby cultivating a more informed public.

\ Despite these positive outcomes and effects, it is important to acknowledge that the simpler-is-better hypothesis was not universally supported (18). While AI-generated summaries were rated higher in terms of credibility and trustworthiness, they were also perceived as less intelligent. This inconsistency underscores the complex interplay between content simplicity and perceived expertise, suggesting that while simpler language can enhance understanding and trust, it might simultaneously reduce perceived intelligence. In science, people may be perceived as smart but untrustworthy and not credible, which suggests a one-size-fits-all model of the relationship between complexity and person-perceptions is perhaps inaccurate.

\ Future research should aim to examine these dynamics further, potentially exploring how different domains of science (e.g., communicating about health, communicating about climate) might uniquely benefit from AI-mediated communication (50). Studies could investigate the long-term impact of AI-mediated communication strategies on public engagement with science and scientists. Finally, texts from only one journal were used in this paper across studies and therefore, texts from other journals should be used as well. As a general science journal that publishes high-impact research, however, using PNAS for this paper was purposeful and helped to ensure fluency effects were investigated across core domains of scientific inquiry.

The author thanks [Redacted] for their thoughts and comments on an earlier draft of this manuscript.

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:::info This paper is available on arxiv under CC BY 4.0 DEED license.

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