Brandon Wang¹ and Julian Avellaneda², ¹USA, ²California State Polytechnic University, USA

Waste mismanagement and recycling contamination represent a significant global challenge, driven largely by public confusion and lack of accessible, real-time guidance. This paper introduces EcoWise, a novel mobile application designed to address these issues by making proper waste disposal intuitive, educational, and engaging. The system uses three core components: a user-facing mobile client, Google's Gemini AI for advanced multimodal classification, and a scalable Firebase backend for real-time data synchronization and gamification. The user flow allows individuals to instantly scan any waste item, receive accurate disposal instructions, and earn rewards, transforming a mundane chore into a positive feedback loop that fosters sustainable habits. To validate the systems' effectiveness, two key experiments were conducted. The first tested the classification accuracy against real-world data, resulting in a 100% success rate and confirming the model's reliability. The second experiment assessed the backend behavioral reward logic, which demonstrated a 76.5% success rate across 17 tested achievements, highlighting the robustness of simple triggers while identifying areas for improvement in complex, time-based functions. The results confirm that Ecowise serves as a powerful, low-cost, and globally scalable model for using artificial intelligence to promote long-term pro-environmental behavior change, offering a viable tool to improve recycling efforts in both developed and underserved communities.

Machine Learning, Computer Vision, Sustainability, Interactive Education