Social media platforms increasingly rely on AI-powered filters and recommendation systems that shape how people see themselves and others. In this lesson, students learn a systematic, five-step process for conducting an algorithm audit by examining TikTok’s AI Manga filter. Using a curated database of inputs and outputs, students form hypotheses, collect and analyze data, and evaluate how algorithmic behaviors may reflect bias or unintended impacts. The lesson emphasizes evidence-based reasoning and helps students see how everyday users can meaningfully question and assess AI systems.

Search engines and other AI systems shape how people see the world, often in ways that feel neutral or objective. However, these systems can reflect and amplify social biases that are embedded in data, design choices, and societal patterns. In this lesson, students investigate algorithmic bias through image search results, using everyday search terms to uncover patterns related to race, gender, and representation. Through discussion, annotation, and reflection, students develop a sense of agency by learning how everyday people can question, audit, and respond to biased algorithmic systems.

Floods are among the world’s most devastating natural disasters, affecting millions of people each year. Today, scientists are using AI flood forecasting models to predict when and where floods may occur, giving communities more time to prepare and stay safe. But these predictions are not perfect. Data can be messy, and humans have many important steps to take in order to build trustworthy AI models that can help with decision-making. In this lesson, students will play Market Marshes, an AI Quest to explore how AI predicts floods, consider challenges with data accuracy, and reflect on the human responsibilities involved in making decisions with AI.

This lesson introduces students to the concept of bias in AI training data and its societal implications. Using a real-world example of Google’s Gemini AI, students will explore how overrepresentation and underrepresentation in datasets affect various groups.

This lesson introduces students to the concept of foundation models, focusing on their role in AI development and the countries at the forefront of their creation. Students will learn the significance of foundation models and explore the implications of a concentrated geographical distribution of these models.

In this lesson, students will consider ethical dilemmas raised by the increasing use of AI in warfare. After listening to a podcast from the New York Times The Daily, about Ukraine’s use of emerging AI technologies in their war against Russia, students will develop their own positions about the ethics of AI warfare through discussion with peers.

This gamified lesson effectively introduces students to the ethical challenges of AI. It boosts critical thinking and ethical reasoning through engaging discussions. It also enhances collaborative skills and decision-making, providing a rich learning experience.

In this lesson, students start to understand the possible consequences of algorithmic bias. They consider a range of applications for which algorithmic bias presents a problem. This could be the 2nd lesson of a 2 part sequence.

In this lesson, students are introduced to the concept of algorithmic bias. Students play a game that illustrates the concept through a hiring simulation. This could be the 1st lesson of a 4 part sequence.

In this lesson, students are introduced to the concept of a digital footprint. They consider data publicly available about them online, including on social media. Then, they consider the ethics of having their data used in AI algorithms.

In this lesson students explore the prevalence of artificial intelligence in their personal lives. As students think through examples of AI in their lives, they explore different concepts of AI to better understand what AI is, what tasks it can do, and how it can do those.