Trash to Tech: AI Smart Bins Building Sustainable Communities

Monday, June 29, 2026 1:00 PM to 2:30 PM · 1 hr. 30 min. (America/New_York)
Poster
Innovative Learning, Making, and Fabrication

Information

This session explores AI-powered smart bins that classify recyclable waste using Google Teachable Machine and webcam vision. Participants will learn how to prototype with Arduino for paper cutting and PET melting into filament, and explore reward systems built with digital platforms. The project bridges sustainability, engineering, and civic engagement.
Role Based Tracks
All LeadersLeaders - DistrictLeaders - TechnologyTeachersLibrarians
Grade Level
PK-12
Transformational Learning Principles
Ignite AgencyPrioritize Authentic Experiences
ISTE Standards
Coaches: Digital Citizen Advocate: Encourage educators and students to use technology to address community challenges.Coaches: Learning Designer: Collaborate with educators to develop authentic, active learning experiences that foster student agency, deepen content mastery and allow students to demonstrate their competency.Students: Innovative Designer: Select and use digital tools to plan and manage a design process that considers design constraints and calculated risks.
Delivery/Output
In Person
Subject
Engineering
Skill Level
Beginner
Outline
Content and Engagement Introduction (10 min) - Present the challenge of waste separation in public spaces. - Share examples of recycling issues in communities and the role of smart cities. - Engage participants with a quick poll: “What’s the biggest challenge in recycling in your context?” Exploring Tools & Concepts (15 min) - Demonstrate how Google Teachable Machine can be trained to recognize recyclable items. - Show prototypes in Arduino for paper cutting and PET melting into filament. - Audience activity: try a pre-trained Teachable Machine demo on their own devices to classify objects. Design Process Workshop (25 min) - Participants work in small groups to sketch their own “smart bin” ideas. - Use provided templates to outline features (identification, reward system, sustainability impact). - Peer-to-peer exchange: groups share their design challenges and brainstorm improvements. Reward Systems & Community Impact (15 min) - Present models of digital reward systems (points, tokens, local government incentives). - Short discussion: “How could a reward system engage your own community?” - Audience creates a quick draft of a reward mechanism using collaborative digital boards (e.g., Jamboard/Miro). Showcase & Reflection (15 min) - Groups present their smart bin designs in 2–3 minutes. - Facilitated reflection: How do these designs prioritize authentic experiences and empower agency? - Closing insights on replicability: how to adapt this project in schools, communities, and local governments. Process & Engagement Tactics - Peer-to-peer interaction: Group work on bin design sketches, sharing prototypes, and feedback. - Device-based activities: Teachable Machine demo, Arduino video demos, collaborative Jamboard/Miro. - Gamified interaction: Polls, challenges, and reward system simulation. - Hands-on engagement: Prototyping exercise where participants connect technology, sustainability, and community impact.
Supporting research
Sharma, S., & Kumar, A. (2023). Artificial intelligence for waste management in smart cities: A review. Environmental Science and Pollution Research, 30(52), 119041–119062. https://doi.org/10.1007/s10311-023-01604-3 Goh, K. C., & Zhang, Y. (2024). Smart bins for enhanced resource recovery and sustainable urban environments. Cities, 148, 104030. https://doi.org/10.1016/j.cities.2024.104030 Patel, R., & Sharma, M. (2021). An intelligent smart bin for waste management. International Journal of Advanced Research in Computer and Communication Engineering, 10(11), 1–6. https://www.researchgate.net/publication/356195661_An_Intelligent_Smart_Bin_for_Waste_Management Mertens, S., & Schmutzler, J. (2021). Smart waste collection processes: A case study about smart waste in a city. Proceedings of the International Conference on Smart Infrastructure and Construction, 341–350. https://publikationen.bibliothek.kit.edu/1000134486/119146391 Dey, A., Paul, A., & Roy, S. (2023). ConvoWaste: An automatic waste segregation machine using deep learning. arXiv preprint arXiv:2302.02976. https://arxiv.org/abs/2302.02976
Attendee Accounts
- Google Teachable Machine - Arduino IDE - Miro or Jamboard account - Tinkercad / Fusion 360
Audience
District-Level LeadershipTeacherTechnology Coach/Trainer
Attendee Devices
Devices useful
Attendee Device Specification
Smartphone: AndroidSmartphone: iOSSmartphone: WindowsTablet: AndroidTablet: iOSTablet: Windows
Presenter Type
Student Presentation

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