Filter by keyword: AI tools in science communication

Realizing the ascribed potential of generative AI for health information seeking depends on recipients’ perceptions of quality. In an online survey (N = 294), we aimed to investigate how German individuals evaluate AI-generated information compared to expert-generated content on the influenza vaccination. A follow-up experiment (N = 1,029) examined the impact of authorship disclosure on perceived argument quality and underlying mechanisms. The findings indicated that expert arguments were rated higher than AI-generated arguments, particularly when authorship was revealed. Trust in science and the Standing Committee on Vaccination accentuated these differences, while trust in AI and innovativeness did not moderate this effect.

AI-generated avatars in science communication offer potential for conveying complex information. However, highly realistic avatars may evoke discomfort and diminish trust, a key factor in science communication. Drawing on existing research, we conducted an experiment (n = 491) examining how avatar realism and gender impact trustworthiness (expertise, integrity, and benevolence). Our findings show that higher realism enhances trustworthiness, contradicting the Uncanny Valley effect. Gender effects were dimension-specific, with male avatars rated higher in expertise. Familiarity with AI and institutional trust also shaped trust perceptions. These insights inform the design of AI avatars for effective science communication while maintaining public trust.

We assessed ChatGPT's ability to identify and categorize actors in German news media articles into societal groups. Through three experiments, we evaluated various models and prompting strategies. In experiment 1, we found that providing ChatGPT with codebooks designed for manual content analysis was insufficient. However, combining Named Entity Recognition with an optimized prompt for actor Classification (NERC pipeline) yielded acceptable results. In experiment 2, we compared the performance of gpt-3.5-turbo, gpt-4o, and gpt-4-turbo, with the latter performing best, though challenges remained in classifying nuanced actor categories. In experiment 3, we demonstrated that repeating the classification with the same model produced highly reliable results, even across different release versions.

Artificial Intelligence (AI) is profoundly reshaping the field of science communication research. We conducted a literature review of 35 articles published between 2002 and 2024, which reveals that research on AI in science communication is still in its infancy but growing, predominantly concentrated in Western contexts, and methodologically inclined toward quantitative approaches. The field largely focuses on communication about AI and public perceptions of AI rather than analyzing actual engagement with generative AI or its systemic impact on science communication ecosystems. To address these gaps, we propose a research agenda centered on four key areas: (1) communication about AI, (2) communication with AI, (3) the impact of AI on science communication ecosystems, and (4) AI’s influence on science, theoretical and methodological approaches.

`AI and Common Sense: Ambitions and Frictions' critically examines the claim of AI with common sense, a notion often assumed in human intelligence but highly debated in machine learning. The book offers a multifaceted exploration, questioning whether common sense is essential for AI and its broader implications for society and the future of technology. Spanning technical, philosophical, and social perspectives, it invites readers to rethink common sense and its role in shaping future AI conversations in society.

Generative AI like ChatGPT has been diagnosed to fundamentally impact different realms of life. This includes science communication, where GenAI tools are becoming important sources of science-related content for many people. This raises the question of whether people trust GenAI as a source in this field, a question that has not been answered sufficiently yet. Adapting a model developed by Roberts et al. [2013] and utilizing survey data from the German Science Barometer 2023, we find that Germans are rather sceptical about and do not strongly trust GenAI in science communication. Structural equation modelling shows that respondents' trust in GenAI as a source in science communication is driven strongly by their general trust in science, which is largely driven by their knowledge about science and the perception that science improves quality of life.

This conference review discusses the July 2024 Public Communication of Science and Technology (PCST) symposium held in South Bend, Indiana, the first PCST symposium held in the United States. It reflects on speakers, topics, and logistics of the event.

Evaluations are becoming more important in science communication. But both science communication practitioners and researchers are not sufficiently utilizing the potential of evaluations yet. In this essay, we first define four requirements for rigorous evaluations of science communication activities and projects. To substantiate our argument, we take stock of the scientific literature, uncover deficiencies in current evaluation practices and identify potential causes. We conclude with laying out how different actors in the field — including science communication practitioners, professional associations, scientific institutions and funding bodies as well as researchers — can contribute to advancing evaluation practices in science communication as well as research on it.

The annual conference of the Science Communication Division of the German Communication Association (DGPuK) was held in Zurich, Switzerland, from 5–7 June 2024. The conference attracted around 125 researchers and science communication practitioners from Europe and beyond. In this review, I provide an overview of the conference and discuss some of the challenges for researching AI in science communication as well as for science communication practice.