Resources

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.