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Calculate Your Carbon Footprint Activity

This activity allows students to use a simple carbon footprint calculator to determine how much carbon their regular activity releases. That number can be used to compare how much carbon is released by AI.

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.  

AI's Impact on My Future Job Activity

In this activity, students watch videos about AI's impact on certain jobs, and then explore how AI might impact the jobs they matched in previous activities.

Open AI News Articles Activity

This activity extends the Generate a Story lesson by asking groups of students to read a "news article" and identify what parts were written by a human and what parts by a GAN. Students then share their conclusions and discuss with the whole class.

Storytelling with GANs Activity

This activity allows students to explore a text generator. It asks them to create an opening sentence of a story (seed text) and use the text generator to complete the story. If time, students also add an image to illustrate their story.

GAN Art! Activity

This activity builds on the whole-class discussion in the Art or Not? Activity to allow students to use the Doodle to Pictures website to create their own "art" and share it on a google slide deck for whole-class discussion.

Art or Not? Activity

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

Generator vs. Discriminator Game Activity

In this activity, students play a game to simulate the adversarial components of a GANs. One team plays the role of a generator. Another team plays the role of a discriminator.

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.  

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).

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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.