Week 6: Artificial Intelligence

Normally we jump right into readings. But this week we want to do something different — and it only works if you do it before you read anything else.

You already have opinions about artificial intelligence. Everyone does. Before we teach you how AI actually works, we want to capture what you think right now: what it is, what it can and can't do, and what it means for your classroom. In two weeks, you will return to your answers and see how your thinking has changed.

Complete the AI Pre-Reflection →

Submit your responses on Blackboard before moving on to Topic 6a.

• Topic 6a – What Is AI?
  Survey the full landscape of artificial intelligence: its origins, its vocabulary, the Turing Test, the distinction between strong and weak AI, and why the field encompasses far more than the tools that have made recent headlines.
• Topic 6b – Reasoning and Search
  Explore how AI systems reason about problems by representing them as state spaces and searching through possible solutions. Understand breadth-first and depth-first search strategies and what it means for an AI to "think through" a problem systematically.
• Topic 6c – Types of Learning
  Survey the three major categories of machine learning — supervised, unsupervised, and reinforcement — and understand what makes each one fundamentally different. This topic is the conceptual map you will use to navigate the rest of Week 6 and all of Week 7.
• Topic 6d – Supervised Learning: Decision Trees
  Work through decision trees as a concrete, traceable supervised learning technique — one you can draw on paper, trace by hand, and adapt for classroom use. See how a tree is built from labeled examples, how it makes predictions, and where its limits lie.
• Topic 6e – Supervised Learning: Neural Networks
  Understand how a single perceptron makes a decision, what happens when its prediction is wrong, and how the learning process adjusts its weights. Then see how layers of perceptrons combine into artificial neural networks — and why their power comes at the cost of interpretability.

Once you have spent time engaging with the CS content, consider it through these additional lenses.

• Social and Ethical Considerations
  Examine how the technical decisions behind decision trees and supervised learning — what data to train on, what reward signals to define, what counts as accuracy — carry real consequences for real people.
• Teaching and Learning Perspectives
  Reflect on how agents, decision trees, and neural networks translate to your classroom. What misconceptions do students bring? What activities make these ideas accessible at your grade level?

You will meet with your small group to make sense of the week's content and prepare for the Competency Demo.

Week 6 Small Group Discussion Instructions