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Bridging Monitoring and Mindset: A Retrieval- Augmented LLM Chatbot Framework for Real¬Time Environmental Education
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This paper proposes a concept for a smartphone chatbot called EcoLitLLM, designed to answer questions about plastic pollution using real-time environmental sensor data and AI. No real-world testing was done: the paper only describes the design and planned features, such as offline use, camera-based litter identification, and citizen reporting.
Plastic pollution is a serious problem for our environment, and many public awareness campaigns are not able to change people's daily habits in a lasting way. At the same time, new Artificial Intelligence (AI) and smart sensor technologies can now monitor rivers, coasts, and ecosystems in real time, but this rich monitoring data rarely reaches ordinary citizens in a form they can understand and act on. This gap between “monitoring” (technical data collection) and “mindset” (public understanding and behavior) is the main motivation of this paper. We propose a conceptual framework named EcoLitLLM, a mobile-based chatbot that uses Retrieval- Augmented Generation (RAG) with a Large Language Model (LLM) to give simple, trustworthy, and location-based answers about plastic pollution and ecosystem health. The framework connects a verified environmental knowledge base with live AI/IoT ecosystem-monitoring feeds, so the chatbot's answers are grounded in current, local, and reliable evidence rather than only static content. The framework also supports offline use through an on-device small language model, multimodal litter recognition (multiple types of data) through the phone camera, and gamified citizen-science reporting that feeds new data back into the monitoring system. Since this is a conceptual paper, the methodology explains the framework-design process and gives logical pseudocode instead of an empirical evaluation. We also discuss expected outcomes, limitations, and directions for future testing of this framework in real communities.
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