Filter by author: Evelyn Jonas

Two studies, using real-time response measurement and interviews, explore how German recipients assess the trustworthiness of a voice-based communicative AI conveying science-related information with empathic and humorous expressions. In both a laboratory and an online study, humor was associated with short-term declines in trustworthiness, reflecting cultural expectations and appreciation of objectivity and neutrality. In contrast, empathic expressions are rated more trustworthy, but evoke less conspicuous effects. Cluster analysis identified four distinct patterns of evaluation, with two groups largely unaffected by affective cues and two skeptical of humor, underscoring the importance of personalization and adaptation in designing trustworthy communicative AI for science communication.

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.

A potential decline in public trust in science has often been linked to digital media environments, which serve as intermediaries of trust by providing cues for why (not) to trust science. This study examines whether exposure to trust cues in content affects public trust in science (across population groups). The study employs a mixed-method design, combining content analysis (“n” = 906) and panel survey data (“n” = 1,030) in Germany. The findings reveal that exposure to trust cues in certain media predicts public trust in science. Variations across trust groups indicate a nuanced nature of trust-assessing processes in digital media environments.

A guiding theory for a continuous and cohesive discussion regarding generative artificial intelligence (GenAI) in science communication is still unavailable. Here, we propose a framework for characterizing, evaluating, and comparing AI-based information technologies in the context of critical engagement with scientific information in online environments. Hierarchically constructed, the framework observes technological properties, user experience, content presentation, and the context in which the technology is being used. Understandable and applicable for non-experts in AI systems, the framework affords a holistic yet practical assessment of various AI-based information technologies, providing both a reflection aid and a conceptual baseline for scholarly references.