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Poster Short Abstract

This study examines how Citizen Science and Artificial Intelligence interact in biodiversity monitoring, analyzing project designs, scientific outcomes, citizen engagement, and broader societal and policy impacts.

Poster Abstract

Citizen Science (CS) and emerging technologies, including Artificial Intelligence (AI), hold significant potential for monitoring biodiversity. However, little is known about the ways CS and AI interact and how these interactions influence scientific, societal, and policy outcomes. Our study addresses this gap in the context of the project “IQ Water: AI-supported Analysis and Prediction of Biodiversity and Water Quality in Drinking Water Reservoirs”. We (i) provide an overview of the different applications of CS-AI in relation to biodiversity and (ii) analyze the design elements of these initiatives and their impacts on scientific results, citizen engagement, and broader societal and policy implications. We conduct a systematic literature review, including a SCOPUS and WEB OF SCIENCE keyword search for peer-reviewed articles, bibliometric analysis to map the field, and in-depth content analysis focusing on applications, project design, and observed outcomes. Results are will be complemented by expert interviews and community feedback at CS- and AI-related conferences. This poster presents the first results and invites discussion on opportunities and challenges for integrating CS and AI in biodiversity monitoring.