AI for High School: Foundations of AI

AI for High School: Foundations of AI

Approximate Total Time: 5 class periods (approximately 45–50 minutes each)

Recommended for Ages 14+ (high school)

About This Unit

In this unit, students develop a foundational understanding of artificial intelligence and how modern AI systems work. Students begin by exploring what AI is, how it differs from traditional computer programs, and where AI appears in everyday life. They then investigate how machines learn from data through supervised, unsupervised, and reinforcement learning.

As the unit progresses, students explore generative AI and how chatbots and large language models generate responses using patterns, prediction, and probability. Finally, students examine the ethical and societal implications of AI by investigating algorithmic bias, fairness, and the human decisions that shape AI systems. Throughout the unit, students develop the knowledge and critical thinking skills needed to evaluate both the capabilities and limitations of artificial intelligence.

Lessons

1. What Is AI?
Students explore what artificial intelligence is and what it is not, distinguish AI from traditional computer programs and automated systems, and use evidence to evaluate real-world technologies.

2. How Do Machines Learn?
Students investigate supervised, unsupervised, and reinforcement learning and examine how AI systems use data to identify patterns, make predictions, and improve over time.

3. What Is Generative AI?
Students compare predictive and generative AI, investigate how generative systems create new content from learned patterns, and consider opportunities and challenges associated with generative AI.

4. What Are Chatbots and How Do They Work?
Students investigate how chatbots and large language models generate responses using patterns learned from large datasets, prediction, and probability, while considering their limitations and ethical implications.

5. Bias in Artificial Intelligence
Students investigate how bias can emerge from data, design choices, and human decision-making, analyze how AI can create unfair outcomes, and evaluate strategies for building fairer, more inclusive AI systems.