The mitigation of the climate crisis demands effective communication strategies. Transforming food systems plays a key role in climate protection, not only by changing eating habits, but also by preventing food waste. While workplaces are commonly used for health promotion activities, they are a rare setting for science communication. This practice insight targets individual food waste reduction through a workshop at the workplace, including a lunch from rescued food, documentary screening, discussion, and expert presentation. It aimed to enhance participants’ self-efficacy and intention to reduce food waste. The effectiveness of the screening was tested experimentally by evaluating the effects of positive and negative framing. Exposure to negative framing was associated with higher intensity in negative affect, whereas positive framing appeared to be associated with higher self-efficacy. Furthermore, this practice insight provides strategies to foster science communication in workplaces.

Despite the rising popularity of video-based platforms, systematic guidelines for developing effective video-based science communication remain scarce. Training scientists in these skills is vital for combating misinformation and engaging audiences. This study reviewed evidence-based strategies for communicating science via video-based social media platforms, identifying 28 articles that included original video-based data and were published in the past decade. Articles were identified through library database searches, journal archives, and publication lists from relevant researchers. Predominantly focusing on YouTube (42.9%) and TikTok (28.6%), qualitative findings revealed best practices related to narrative structure, emotion and connection, video features, professionalism and quality, and social media strategies. Highlighting actionable strategies, this research provides valuable insights for scientists navigating the dynamic landscape of video-based science communication.

Signs used for science interpretation within national parks have been little studied. We analyzed the textual content of 129 signs in 11 US national parks. Science content was high, but readability was low overall and inversely related to the amount of science content. The amount of science varied by subject area and national park, as did the depth of information and its relevance to humans. Colorado’s Great Sand Dunes National Park, however, had signs containing high amounts of science with the highest readability scores, emphasizing the potential benefits for science communication that can come from understanding the science of signs.

Artificial Intelligence (AI) is fundamentally transforming science communication. This editorial for the JCOM Special Issue “Science Communication in the Age of AI” explores the implications of AI, especially generative AI, for science communication, its promises and challenges. The articles in this Special Issue can be categorized into 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. This collection of articles advances empirical and theoretical insight into AI’s evolving role in science communication, emphasizing interdisciplinary and comparative perspectives.

This paper studies how artificial intelligence was set to the agenda in the press and social media in France. By simultaneously analysing the framing of AI and the key actors who dominated the discourse on this technology in the national press and on the X and Facebook platforms, the study highlights, on the one hand, the influence of digital companies and government narratives, and on the other, the presence of alternative stakeholder perspectives that diverge from dominant discourses and contribute to political polarisation on AI-related issues such as facial recognition. Our study sheds light on how AI framing can highlight dominant and alternative narratives and visions and may contribute to the consolidation of socio-technical imaginaries in the French public sphere.

Engaging with the ongoing debate regarding the portrayal of artificial intelligence (AI) in the public sphere – particularly the alleged predominance of sci-fi imagery and humanoid robots – our study examines how six German print media visualize articles related to AI. A mixed-methods approach combines qualitative and quantitative visual content analysis, analyzing 818 images from articles published in 2019 and 2022/23. Our findings indicate that human figures, rather than robots, serve as dominant visual objects, and no pronounced gaps between textual and visual representations of AI were observed. Overall, German print media appear to present a differentiated perspective on AI, balancing opportunities and risks associated with this technology.

This paper examines how artificial intelligence (AI) imaginaries are negotiated by key stakeholders in the United States, China, and Germany, focusing on how public perceptions and discourses shape AI as a sociotechnical phenomenon. Drawing on the concept of sociotechnical imaginaries in public communication, the study explores how stakeholders from industry, government, academia, media and civil society actively co-construct and contest visions of the future of AI. The comparative analysis challenges the notion that national perceptions are monolithic, highlighting the complex and heterogeneous discursive processes surrounding AI. The paper utilises stakeholder interviews to analyse how different actors position themselves within these imaginaries. The analysis highlights overarching and sociopolitically diverse AI imaginaries as well as sectoral and stakeholder co-dependencies within and across the case study countries. It hence offers insights into the socio-political dynamics that influence AI’s evolving role in society, thus contributing to debates on science communication and the social construction of technology.

This study examines the adoption of generative AI (genAI) tools in German university communication departments using 2023 and 2024 survey data. Adoption has significantly increased in 2024, particularly for text generation, with private universities leading the way. Efficiency gains are evident, but issues with factual accuracy and data privacy persist. The findings highlight a transition from cautious experimentation to mainstream integration of genAI in communication strategies, though ethical concerns remain. Communication departments face the challenge of balancing genAI’s efficiency benefits with the need to uphold quality, individuality, and privacy.

The advent of generative Artificial Intelligence (genAI) is expected to have a significant impact on journalism. In this study, we address whether this development could help mitigate the crisis in science journalism. We conducted semi-structured interviews with 30 German science journalists, asking them about the potential impact genAI may have on the news-making process (i.e., selection, production, and distribution). The results suggest that interviewees anticipate many future benefits associated with genAI, some believe that the technology is unlikely to worsen the crisis in science journalism, while others express concerns about potential negative consequences (e.g., job loss).

This study explores the role of ChatGPT in science-related information retrieval, building on research conducted in 2023. Drawing on online survey data from seven countries—Australia, Denmark, Germany, Israel, South Korea, Taiwan, and the United States—and two data collection points (2023 and 2024), the study highlights ChatGPT’s growing role as an information intermediary, reflecting the rapid diffusion of generative AI (GenAI) in general. While GenAI adoption is a global phenomenon, distinct regional variations emerge in the use of ChatGPT for science-related searches. Additionally, the study finds that a specific subset of the population is more likely to use ChatGPT for science-related information retrieval. Across all countries surveyed, science-information seekers report higher levels of trust in GenAI compared to non-users. They also exhibit a stronger understanding of how (Gen)AI works and, with some notable exceptions, show greater awareness of its epistemic limitations.