Document Type
Article
Publication Date
7-21-2025
Publisher
Association for Computing Machinery
Abstract
We present Waste Genie+ (WG+), an AI-infused web-based educational technology to improve sustainability awareness and waste management skills. WG+ utilizes Large Language Models (LLMs) to transform complex environmental regulations and waste management information into accessible and digestible content, enabling learning in the complex, legislative-involved sustainability field. WG+ features AI-infused content, interactive quizzes, and various sustainability awareness simulations (i.e., waste classification scanner, virtual carbon credit tracker, carbon dioxide emission converter). We used LLM chain-of-thought reasoning to implement a systematic prompt-based evaluation of the decomposed legislative content quality. A 10-day user study with 10 participants was designed and conducted to evaluate the effectiveness of WG+ in improving users’ understanding of waste management practices and regulations. Results demonstrated that our Environmental Legislative-guided LLM successfully extracted coherent, highly readable content while preserving the original legal information. The results also indicated a significant improvement in the participants’ sustainability awareness and waste sorting abilities. It also revealed that users found the quizzes engaging, and the AI-generated bite-sized content was more digestible and easier to understand compared to the original bills or articles. These findings contributed to our understanding of how AI-enhanced educational technologies can support continuous informal learning in this growing, convoluted field and promote environmental stewardship, facilitate public awareness of sustainability practices and policies.
Recommended Citation
Sun, Q., & Hsiao, I.-H. (2025). AI-infused Educational Technology for Continuous Waste Management Learning: An Environmental Legislative-guided LLM enhanced approach. Proceedings of the 2025 ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, COMPASS ’25, 311–324. https://doi.org/10.1145/3715335.3735473
Comments
Open access to this article is funded by Santa Clara University Library.
This work is licensed under a Creative Commons Attribution 4.0 International License.