How Do Machines Learn?: Ages 11-13

In this unit, students will explore how machines learn by diving into the basics of supervised machine learning. They’ll begin by learning how computers are trained using labeled data. Through hands-on activities and class discussions, students will identify real-world problems within their school that could be addressed using machine learning. Working collaboratively, they will use Google’s Teachable Machine to train a simple model that classifies images based on examples they provide. This experience helps them understand how AI systems recognize patterns, make predictions, and improve with more data. As the unit progresses, students will shift their focus to understanding bias in artificial intelligence. They will learn how a machine’s “perspective” is shaped entirely by the data it receives, and how unbalanced or limited datasets can lead to unfair or discriminatory outcomes. By analyzing real-world examples, including a video about researcher Joy Buolamwini, they will consider how algorithmic bias can affect people’s lives. Students will participate in an interactive activity to identify potential biases in a fictional school-based AI system and reflect on ways to ensure fairness. This unit equips students with foundational AI literacy, emphasizing both how machines learn and the responsibility we have in designing fair and inclusive AI systems.

How Do Machines Learn?: Ages 14 and up

The unit “How Do Machines Learn?” introduces high school students to foundational concepts in machine learning and artificial intelligence through engaging, age-appropriate lessons. Throughout three lessons, students explore how machines can learn from data, simulate the functioning of neural networks, and critically examine the impact of bias in AI systems. By participating in interactive activities and group discussions, students learn to distinguish between supervised, unsupervised, and reinforcement learning, simulate how data flows through a neural network, and analyze real-world examples of bias that can affect the fairness and accuracy of AI technologies.

Throughout the unit, students are encouraged to think critically about how AI systems are built and the human choices that influence their outcomes. Rather than focusing solely on technical knowledge, the lessons emphasize ethical reflection, collaborative learning, and the development of computational thinking skills. By the end of the unit, students will not only understand how machines learn but also recognize their role in shaping the future of technology to be more inclusive, responsible, and fair.