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.
After this session, participants will be able to:
Design and prototype AI-powered smart bins using Google Teachable Machine and Arduino to classify recyclable materials.
Apply digital tools to manage design processes that address real-world constraints, such as paper cutting and PET filament production.
Explore reward-based systems that motivate civic engagement and encourage sustainable waste practices.
Collaborate in interdisciplinary teams that integrate perspectives from education, government, and community for impactful solutions.
Reflect on how authentic, technology-driven projects can promote agency, sustainability, and innovation in smart city initiatives.
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
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