AI-supported science communication through the lens of perception, knowlede, acquisition, and credibility

This contribution examines how artificial intelligence is reshaping science communication, both as a subject of public discourse and as a tool for communicative and research practices. Generative AI can improve access to scientific knowledge, support journalists and researchers in tasks such as summarization, translation, and analysis, and open up new methodological possibilities. At the same time, it raises concerns regarding accuracy, transparency, source attribution, and the changing role of science journalism.

A particular focus lies on diversity and the public visibility of scientific expertise. AI systems may reproduce existing inequalities and stereotypical representations embedded in their training data, but they may also help broaden the range of experts considered in science communication when deliberately designed and prompted to promote diversity. Rather than replacing established actors, AI is therefore likely to transform and reorganize existing relationships between scientists, journalists, and audiences. The central challenge will be to ensure that AI-supported science communication strengthens accuracy, transparency, and diversity of voices rather than undermining them.