Find Resources

Neural Network Game Activity

Students simulate the working of a neural network by choosing 4 words to describe an image, which are fed forward through the network, and the results evaluated.  The input weights are adjusted (back propagation) and the process is repeated with a second image.  

Thumbs-Up/Thumbs-Down

This activity introduces Teachable Machines and has students follow the "Teachable Machines Tutorial" to create training data sets and test the algorithm using the camera on the computer.

Career Daydreaming Activity

In the Career Daydreaming Lesson, students are led through a guided script to focus on the activity.  In this activity, students daydream about their future job and how it might be affected by AI.  

AI Investigation Activity

After a review of concepts of bias and classification systems, students look at examples of AI and identify bias in them, including:
Google image search results for “physicist”.
Google translate “she is a doctor, he is a nurse” from English to Hungarian and back to English.
Explore QuickDraw’s database of faces.
Google image search results for “outdoor recreation”

Pasta Land Activity

Students create their own decision trees that can be used to classify various types of pasta. This activity introduces the concepts of a decision tree, classification, and bias.

Ethical Matrix Activity

This activity asks students to create their own ethical matrices for their best PB&J sandwich algorithms.  It shows that different algorithms can have different purposes for different stakeholders and that such relationships can be visually represented using an ethical matrix. 

Best PB&J Activity

This activity asks students to write an algorithm for the "best" pb&j sandwich, and so introduces the concept of designing an algorithm to meet one value over another (optimizing).

Redesign Youtube

3.5 Redesign Youtube Lesson

In this lesson, students will redesign the YouTube recommendation algorithm to meet their needs and reduce bias. This is a culminating project that can span several days of work and spark student reflection on lessons learned from the curriculum.

Environmental Impact of AI

3.4 Environmental Impact of AI Lesson

In this lesson, students will explore the environmental impact of training AI models. Students will learn that the design of AI algorithms can have consequences for the environment.

Spread of Misinformation

3.3 Spread of Misinformation Lesson

In this lesson, students will be able to tell what misinformation is and understand that it spreads faster than authentic information. In the first lesson, students will play out a game in which they spread misinformation and reflect on their choices. In the second one, they will learn how to spot misinformation and come up with solutions on how to stop it.

Deepfakes

3.2 What are Deepfakes? Lesson

In this lesson students will explore what deepfakes are, how realistic they can look, and ways to identify them. Students will learn how deepfakes are made and several strategies to identify them.

Unanticipated Consequences

3.1 Unanticipated Consequences of Technology Lesson

This lesson introduces students to the potential consequences of AI technologies and shows them that such consequences may or may not be the ones we intended or anticipated. Students will learn that AI technologies can have unanticipated effects on seemingly unrelated systems (e.g., social, cultural, environmental, etc.)

Storytelling with AI

2.4 Generate a Story Lesson

In this lesson, students will create stories of their own with GAN text and art tools. Students will learn to use GAN-based generation tools to generate texts and form them into stories.

What is art?

2.3 AI Generated Art Lesson

In this lesson, students will explore various forms of AI-generated art. They will engage in a conversation about what is art and who can make art.

Generator vs Discriminator

2.2 How do GANs Work? Lesson

This lesson introduces students to how GANs work as a result of the interplay between generator and discriminator neural networks. Students will learn how the generator and discriminator compete with one another to generate text, images, videos, and more.

GANs or Not

2.1 What are GANs? Lesson

In this lesson, students will learn that GANs can generate art such as photographs, paintings, handwritten poetry, music, and jokes (that are kind of funny! Maybe.)

Inventory of Me

1.4 Inventory of Me Lesson

In this activity, students will learn about Holland’s work personality types and examples of jobs favored by people with each type. This lesson requires the students to use the Internet to answer a survey and explore a website.

Search for "[keyword]" across the whole site.
If you can't find what you're looking for, send us a comment about what you were expecting to find.

Unless otherwise licensed, all works accessible here are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-NC). Everyday AI is partially funded by the National Science Foundation.