Intro to AI and ML Curriculum

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1. What is AI and ML?

• Objective: 
  • Introduce students to the concepts of Artificial Intelligence (AI) and Machine Learning (ML).
• Topics Covered:
  • What is AI? Understanding how computers mimic human intelligence.
  • What is ML? How machines learn from data without being explicitly programmed.
  • Real-life examples of AI and ML: Voice assistants, self-driving cars, recommendation systems.
• Hands-On Activity: AI in Everyday Life
  • Students will discuss different AI technologies they’ve encountered (e.g., Siri, Alexa, Netflix recommendations) and make a collage of real-world examples of AI and ML in action.

2. How Machines Learn

• Objective: 
  • Explore the basics of how machines learn from data.
• Topics Covered:
  • What is data? Understanding data collection and how machines use it.
  • How do machines learn patterns from data? Introduction to supervised learning.
  • Key ML terms: Training data, model, and prediction.
• Hands-On Activity: Sorting Activity (Supervised Learning)
  • Students will participate in a sorting game where they teach the "machine" (a classmate) to recognize and sort shapes or objects by their features (e.g., size, color). This simulates how machines learn from examples.

3. Types of Machine Learning Models

• Objective: 
  • Introduce students to different types of ML models.
• Topics Covered:
  • Supervised learning: Teaching a machine with labeled data.
  • Unsupervised learning: Finding patterns in data without labels.
  • Reinforcement learning: Machines learn by trial and error through rewards.
• Hands-On Activity: AI Model Match Game
  • Create flashcards with different scenarios (e.g., recommending movies, grouping similar pictures, teaching a robot to play a game), and students will match each scenario to the appropriate type of ML model.

4. Coding Introduction to AI – Python Basics

• Objective: 
  • Introduce students to basic Python coding concepts for AI.
• Topics Covered:
  • Variables and data types (strings, integers).
  • If statements and loops.
  • Simple programs that use logic to make decisions.
• Hands-On Project: Magic 8-Ball AI
  • Students will create a Python program that mimics a Magic 8-Ball, giving random answers to yes/no questions. This introduces the concept of randomness in AI.

5. Building a Simple Machine Learning Model

• Objective: 
  • Teach students how to build a basic machine learning model.
• Topics Covered:
  • Data collection and preparation.
  • Training a simple model to recognize patterns (e.g., classifying animals or objects).
  • Introduction to scikit-learn for building models in Python.
• Hands-On Project: Image Classifier
  • Using a pre-made dataset (e.g., animals or fruits), students will use Python to train a simple ML model that can classify different images.
  • Discuss how the model improves its accuracy with more training data.

6. AI in the Real World and Future of AI

• Objective: 
  • Explore how AI is used in the real world and what the future holds.
• Topics Covered:
  • AI in healthcare, education, robotics, and transportation.
  • Ethical concerns: Bias in AI, privacy, and the impact of AI on jobs.
  • The future of AI: How AI can be used to solve global challenges (e.g., climate change, space exploration).
• Hands-On Activity: Design Your Own AI
  • Students will brainstorm and design an AI system that solves a real-world problem (e.g., an AI for better recycling or an AI tutor for students).
  • Present their designs and explain how the AI would learn and function.
• Materials: Paper, markers, craft materials for building mock models.

Assessment and Reflection:

• At the end of each lesson, students will write down one key thing they learned and one question they still have about AI or ML.
• Students can choose to either modify their Magic 8-Ball AI project or expand their Image Classifier project. They can present the improved version and explain what they changed.