Framework for AI Integration in Citizen Science: Insights From the SKILIKET Project

Abstract

Citizen science projects that use Internet of Things (IoT) devices are transforming environmental education by enabling real-time, participatory data collection. However, few initiatives integrate Artificial Intelligence (AI) to support the analysis and prediction of environmental dynamics, as well as their interpretation and deeper learning outcomes. This article presents a framework for incorporating AI into IoT-based citizen science educational systems, exemplified by the SKILIKET project. SKILIKET combines quantitative sensor data (e.g., temperature, CO2, humidity, UV, and noise) with qualitative human observations (e.g., perceived smells, sounds, and visual cues) collected via a mobile app to help participants better understand socioecological phenomena in their environments. Using a Design-Based Research (DBR) approach, the study explores AI functionalities that could support environmental interpretation, predictive analytics for heterogeneous environmental data, and conversational agents for reflective learning. Preliminary tests show that AI-powered predictive models aid pattern recognition and foster participant reflection. The proposed framework outlines principles for modular AI integration, emphasizing user-centered design, ethical data practices, and alignment with STEM education goals. It establishes a foundation for AI-supported citizen science education, aiming to foster critical thinking, civic participation and proactive environmental stewardship.