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AI Career Portfolio Activity

This activity suggests that if several of the AI careers activities will be used, the student work could be collected into a career portfolio.

My Future Job Roadmap Activity

In this activity, students take time to complete their own career roadmaps, answering questions, exploring the Job Interest website and watching videos of AI’s role in different careers. If a student finishes earlier, they can move ahead and start another roadmap for a different job interest.

Spotting & Stopping Misinformation Activity

This activity is an optional extension of the Spread of Misinformation Lesson, which gives students more time to think in small groups bout how to spot and stop the spread of misinformation in their communities.

Deepfakes Activity

In Day 1, students try to identify deepfakes from slides.  Following additional instruction, on Day 2 students now see if they are better able to identify other deepfakes.  

Art or Not? Activity

A whole-class activity where students share their impressions of slides and whether or not they are "art".

GANs or Not? Activity

In this activity, students view pieces of art and try to guess if they are generated by AI or not generated by AI.

Inventory of Me Activity

In this activity, students learn about Holland’s 6 work personality types, predict and confirm their own work personalities, and find a list of jobs that people of their work personalities usually enjoy doing.

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.  

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.  

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.

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.

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.)

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.)

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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.