6-8:05:Introduction to Machine Learning Models and Data Quality

Module 2 goes under the hood. How do machines learn? Students meet the idea of a model, teach a machine with examples, and see why data quality decides what AI gets right – and wrong.

This is the lesson that turns “AI is magic” into “AI is a model trained on data”. Once students hold that idea, they can reason about why AI fails, who is responsible, and how to make it better – the theme of the whole module.

What students do
• “How Do Machines Learn? Welcome to Module 2!” – demos of classification and regression in plain terms
• Teaching Machines: supervised and unsupervised learning, and what a “model” is
• Let’s Explore – How Does AI Learn? Good datasets versus poor ones
• Mini Case Study – “Can AI Be Unfair?”: a first look at bias, e.g. facial-recognition errors

By the end, students can
• Model, training data and prediction
• Garbage in, garbage out
• A first look at unfairness in AI

Learning focus (Fink’s Taxonomy of Significant Learning): Foundational Knowledge · Caring.

Video and interactive content with a case study. Part of Grades 6–8: AI Agents in Action, Module 2 – How Machines Learn – Patterns, Data & Bias, Week 1. Next lesson: Hands-on Model Training Simulation and Troubleshooting.