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What is AI? Storyboard Teacher Made

This is a framework and exemplar to use if you want to have students write a short storyboard of a potential AI tool. This is useful for integrating ELA into this lesson, including story elements.

Online version of Neural Networks Activity

An online version of the Neural Network Activity, where 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.

Offline

A hands-on version of the AI or Not? sorting activity in which students sort pictures of contemporary technology into two groups: technology with AI and technology without AI. The activity is designed to get students thinking about the three parts of machine learning (dataset, learning algorithm, prediction) as a tool for determining whether the technology in their everyday life uses AI or not.

Prediction Matching Activity

This activity introduces students to data classification and prediction, by having students play a game (hands-on or using Nearpod) to predict whether pieces of technology use AI or not. It is an introduction to the first Teachable Machine Activity (Thumbs-up/Thumbs-Down).

AI Career Portfolio Activity

The AI Career Portfolio Activity includes this specific activity and others, and is an alternative to this stand-alone activity.

GANs Exploration! Activity

This activity allows students to explore online GANs tools (GANimals, Sketch RNN, Pix2Pix, AI Duet, and handwriting) to determine how GANs work.

Re-Curate a Dataset

This activity builds on the Cat/Dog Classifier Teachable Machine Activity to have students re-curate the data to improve the algorithm.  

Cat/Dog Classifier Activity

This activity builds on the Thumbs-Up/Thumbs-Down Teachable Machine Activity to have students upload photos of cats and dogs to train Teachable Machine, and then test the algorithm with new data.

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.

"Is it for Winter?" Activity

This Activity continues from the PastaLand Activity, and asks students to design decision trees that group clothing by their shared traits or features that make them belong in a category of Winter or Not Winter clothes. On the second day, students are asked to classify the clothes using only information contained in an SKU barcode.

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.

Youtube Redesign Activity

This activity challenges students to redesign the YouTube recommendation algorithm to meet their needs and reduce bias.

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.

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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.