In this lesson, students explore facial recognition technology as a “classification task.” Each time a smartphone uses facial recognition, it asks, “Is this me or not me?” Building on lesson 1, where students learned that images are numerical representations of pixel values, this lesson helps students visualize images in multidimensional space, with dimensions equal to the number of facial variables or features in the model.

This lesson introduces high school students to facial recognition and machine learning in statistics. Students will explore how images serve as quantitative data and learn about facial recognition through image comparison, similarity, and error thresholds.

In this interactive lesson, students will learn about AI prompting by designing, customizing, and testing their own chatbot. They will consider how instructions impact AI’s performance, and they will discuss the potential and risks of creating custom chatbots.

In this lesson, students will learn how AI is used to analyze whale soundwaves and detect patterns by identifying similar sounds. Students will have the opportunity to listen to real whale sounds and explore a research tool designed by Google, the National Oceanic and Atmospheric Administration (NOAA), and the Pacific Islands Fisheries Science Center (PIFSC). Through this exploration, students will consider the role of AI in marine research and understand why this technology is vital for conservation efforts.

This lesson explores the ethical implications of generative AI, focusing on its impact on creativity and academic integrity. Through discussions, group activities, and a structured debate, students will explore the legal arguments surrounding generative AI’s use of public sources and form opinions about what constitutes plagiarism in AI-assisted writing.

In this lesson, students will explore how AI tools like ChatGPT can assist with narrative writing. They will assess the strengths and limitations of using AI for creative writing and learn how to balance AI capabilities with their own creativity.

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 learn about language models and why AI models like ChatGPT are called “Large Language Models” (LLMs). They will develop a fundamental knowledge of what makes an LLM “large” and how parameters are important to understanding an LLM’s capabilities, data needs, and power requirements.

In this lesson, students will gain a general overview of various Large Language Models (LLMs), their functions, and their applications in everyday life. By the end of the lesson, they will be able to explain what an LLM is, recognize its uses, and identify some of the popular LLMs currently available.

In this lesson, students will apply their knowledge about healthy ecosystems to understanding ways that AI can help conserve the natural world. After studying several example applications, they will engage in a discussion and simulation to evaluate the effectiveness of different AI applications in natural conservation.

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.

This lesson explores the impact of biased data on AI systems with a focus on the Global South. Students will learn how biased data leads to biased results since AI reflects the data it is trained on. In particular, students will focus on the impact of this on countries that are generally less industrialized and have lower income levels than developed nations in the Global North.

In this lesson, students explore AI “under the hood” by learning how sensory information is converted into data and used for mathematical operations that make an AI “intelligent”. Students will learn that visual data has features like brightness, and distance (like between parts of a face) and is used in applications like facial recognition. Along the way, students will be able to connect these technical processes to social consequences such as bias, and learn to treat AI with skepticism.

In this lesson students will explore AI created music and lyrics and compare it to human generated music. Students will analyze how well AI can mimic a genre/artist/song and what potential ethical controversies this could cause in our society. This lesson is used as a springboard for evaluating how AI can have both positive and negative effects and student’s ability to recognize both sides.

In this lesson, students compare AI and human-generated texts to analyze the affordances and limitations of large language models. They’ll score, guess origins, and discuss differences, then decide which aspects to adopt or avoid in their writing. The lesson culminates in a discussion of what aspects of the AI-generated writing students should emulate in their own writing and what aspects they should avoid.

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 will watch an online video presenting the history of artificial intelligence, which goes back to the 1950s. They will also learn how current artificial intelligence technologies build on that history.

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 will explore the idea of Artificial Intelligence (AI) by looking at a concrete example of AI which uses machine learning to distinguish objects in an application called Teachable Machines.

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.