Abstract:
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.
