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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.

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.

Based on an ethnography of the development and production of science YouTube videos – a collaboration between a German public broadcaster and social science scholars – we identify three intermediary steps through which recommendation algorithms shape science content on social media. We argue that algorithms induce changes to science content through the power they exert over the content's visibility on social media platforms. Change is driven by how practitioners interpret algorithms, infer content strategies to enhance visibility, and adjust content creation practices accordingly. By unpacking these intermediate steps, we reveal the nuanced mechanisms by which algorithms indirectly shape science content.

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.

Most public audiences in Germany receive scientific information via a variety of (digital) media; in these contexts, media act as intermediaries of trust in science by providing information that present reasons for public audiences to place their trust in science. To describe this process, the study introduces the term “trust cues”. To identify such content-related trust cues, an explorative qualitative content analysis has been applied to German journalistic, populist, social, and other (non-journalistic) online media (“n” = 158). In total, “n” = 1,329 trust cues were coded. The findings emphasize the diversity of mediated trust, with trust cues being connected to dimensions of trust in science (established: expertise, integrity, benevolence; recently introduced: transparency, dialogue). Through this analysis, the study aims for a better understanding of mediated trust in science. Deriving this finding is crucial since public trust in science is important for individual and collective informed decision-making and crises management.