Design Tasks Around Customization

This project helps you move beyond following examples to create your own customized AI tool that solves a real-world problem. You’ll learn to design, develop, and prototype AI solutions that fit specific needs in your school or community.

Learning Objectives

• Problem-Solving:
  • Identify real-world challenges in your school or community.
• Customized Design:
  • Design an AI solution tailored to the specific problem.
• Technical Skills:
  • Learn to set up data structures, choose the right model, and create a user-friendly interface.
• Prototyping:
  • Build a Minimum Viable Product (MVP) using either low-code tools or full programming.

Example Activities

1. Defining a Problem:

   • Task: Work in teams to select a problem that matters to you.
   • Examples:
     • A book recommendation system for your school library.
     • A tool to predict and manage traffic congestion in your local area.
   • Goal: Clearly describe the problem and what you hope to solve.

2. Customized AI Design:

   • Plan Your Solution:
     • Data Structure & Preprocessing: Decide how to collect and prepare your data.
     • Model Selection: Choose an AI model that fits your problem.
     • User Interface (UI/UX): Sketch or design how users will interact with your tool.
   • Activity: Create a design blueprint or flowchart that outlines your AI system from start to finish.

3. Development & Prototyping:

   • Choose Your Environment:
     • Decide whether to use a low-code platform (like App Inventor or Power Apps) or write your own code (using Python, for example).
   • Build the MVP:
     • Focus on developing a working prototype that demonstrates your idea.
   • Activity: Develop your prototype, test it with real or simulated data, and note any improvements needed.

4. Presentation and Feedback:

   • Share Your Work:
     • Present your project to classmates or a panel of teachers.
   • Discuss Improvements:
     • Talk about what worked, what didn’t, and brainstorm ways to make it even better.
   • Goal: Learn from feedback to refine your design and approach.

Key Takeaways

• Customization is Key:
  • Rather than simply following examples, you create an AI tool that meets a real need.
• End-to-End Learning:
  • Gain experience from problem definition, design, and prototyping to final presentation.
• Collaboration:
  • Work in teams to brainstorm, design, and build a solution—learning from each other along the way.
• Practical Skills:
  • Whether using low-code platforms or full programming, focus on creating a Minimum Viable Product that shows your solution in action.

By completing these tasks, you’ll not only understand how AI works but also how to adapt and customize it to solve problems that matter to you and your community.

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