Sammy At School

A POV-style game for kids where simple choices shape the world. Help Sammy make eco-conscious decisions in daily life, with an object detection feature to identify degradable vs non-degradable items.

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Inspiration

The inspiration for this game came from observing how even the smallest life actions are contributing to the larger state of the environment. Be it water wastage or improper waste disposal, we realized educating the future generation is the needed for shaping a sustainable future. We wanted to create an engaging yet effective way of teaching eco-consciousness - an immersive learning experience.

What it does

They decide the choices in their daily lives impacting the environment—all without being explicitly told what’s right or wrong.The twist? Each decision subtly affects their environment, and by the end, they either help create a thriving, happy planet or witness a dystopian reality. To make learning fun and intuitive, the game features an integrated object detection camera that identifies items and categorizes them as degradable or non-degradable—helping children understand real-world sustainability

How we built it

We started with designing the game narrative and mechanics, focusing on the daily choices in conserving water, eco-friendly materials, and segregation of garbage. Using any game engine, we developed an interactive, narrative-driven environment with multiple outcomes based on the user's choices. To make the learning immersive for players, we have integrated an object detection system using models of machine learning. This system identifies real-world objects through a camera and categorizes them into recyclable, compostable, or non-recyclable items to help kids understand waste management visually and practically.

Challenges we ran into

One of the biggest challenges was how to keep the game entertaining while delivering an educational message. We had to work hard to find the right balance between fun and learning. Integrating the object detection system also proved tricky; training the model to recognize a wide range of objects accurately was time-consuming and required fine-tuning.

Accomplishments that we're proud of

We are proud that as a team we have overcome many challenges throughout our code but as a team we work together and fix it. We learned many new technologies and strategies on how to go about our problems in a logical way. Having been able to develop an integrated object detection camera, which gives the user interactivity for learning waste segregation, is an important milestone that we are proud of!

What we learned

We practically implemented PyGame in Python, utilized OpenCv, ROboFlow and MediaPipe for integration of object detection feature in our game. We also picked up some theories on behavioral psychology, most especially choices and consequences leading to our behavior developing over time. Incorporating object detection into the mix really showed us how we can take and innovative idea and turn it into a learning experience.

What's next for Sammy At School

Moving forward, we will:

  • Expand the game by adding more scenarios and daily choices affecting the environment.
  • Enhance the object detection model to identify more varieties of items for waste segregation.
  • Work with educators to integrate the game into school curricula as an extra activity as a tool for teaching sustainability.
  • Multiplayer mode can be developed where players come together to build a virtual sustainable community and compete in various eco-friendly challenges.

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