|Title||Resource Link||Summary||Additional Details||Learning Outcomes||Grade level(s)||Ethical Principles||Curriculum Connections||Added by|
|AI Duet||https://experiments.withgoogle.com/ai/ai-duet/view/||an AI activitiy that will respond to your piano playing, by playing an accompanying duet||You can use this activity to help students learn how to accompany a piece being played by their peers, and play a duet themselves. It can also be used as a study as to WHY the AI makes the decisions it does, and what kind of training data it could have picked up these behaviours from. These are key topics for students to explore as they can help students answer the questions of "How should I interact with AI in a safe & ethical manner" and "How closely can an AI mimic human behaviour, and what kinds of things does it struggle with"||How relieably can an AI do what a human does?||General||How closely should AI replicate human behaviour?||Music theory||Moh|
|Predict a Pie||https://nn.inventor.city/||In this workshop, we are introducing the concept of Artificial Intelligence (AI) and how it is able to make predictions using a neural network. Similar to how the human brain makes decisions using the neural network process, AI uses neural networks to predict things such as the weather, climate change effects, the stock market, etc.||In this workshop, we will take a look at the basic building blocks of neural networks and build our own neural network using pie ingredients!|
The activity begins with you becoming a baker and you want to become the best baker in town. But you have way too many ingredients and coming up with combinations with each ingredient will just be too time-consuming. We can use AI neural networks to help us come up with delicious recipes!
First, you will learn how a neural network works by building a simple network with ingredients of your choice. You will start by having two simple outputs such as “delicious pie” and “other”. Then you will learn how to connect different nodes together to get the output you want with your chosen ingredients. You will also see how not every combination of ingredients actually would be delicious and how some unexpected combinations can turn out to be delicious. You will learn to do exactly what a neural network AI does and how it makes predictions based on random ingredients.
|Neural Networks, Importance of training data||General||Bias in training data, inclusitivity in AI design||adaptable||Moh|
|Teachable machine||https://teachablemachine.withgoogle.com/||A fast and easy way to create AI models using vision recognition, or sound recognition that can be personalized & fun||Vision Recognition, Importance of training data||General||Bias in training data on images||adaptable||Brenda|
|Dancing with AI||https://dancingwithai.media.mit.edu/||Various Interactive visual activities using vision recognition AI to help students||Physical movement is one of the most engaging ways to interact with AI systems, but it’s rare today to see motion integrated with K-12 AI curricula. Beyond that, many middle schoolers have passionate interests in dance, art, physical movement in sports, and video games that involve physical motion (Beat Saber, Just Dance) which aren’t easy to build on in the typical creative learning environments found in classrooms. Dancing with AI is a week-long workshop curriculum in which students conceptualize, design, build, and reflect on interactive physical-movement-based multimedia experiences. Students will learn to build interactive AI projects using two new Scratch Extension tools developed for this curriculum: (1) hand/body/face position-tracking and expression-detecting blocks based on the machine learning models PoseNet & MediaPipe from Google and Affectiva’s face model, and (2) Teachable Machine blocks that allow students to train their own image- and pose-recognition models on Google’s Teachable Machine and use them as part of their projects.|
The goal of this curriculum is to engage students with interactive lessons and projects, and to have them think critically about AI and natural interaction. Throughout this course, students will have open-ended discussions on questions such as:
- How do we compare and contrast forms of representation?
- How do we interact with other humans vs. how do we interact with AI?
- What are forms of bias that can arise from improperly trained machine learning models, and how can we remediate those biases?
- What kind of projects can you create with interactive AI that will benefit your community?
These questions will allow students to reflect on their own abilities as consumers and creators of interactive AI, and have them think critically about the ways it can help and harm society. (description from website)
|Scratch programming, vision recognition||General||Diversity in vision recognition||Dance, Computer Science||Brenda|
|https://docs.google.com/document/d/1e9wx9oBg7CR0s5O7YnYHVmX7H7pnITfoDxNdrSGkp60/view||MIT. set of activities, teacher guides, assessments, materials, and more to assist educators in teaching about the ethics of artificial intelligence.||Set of activities, teacher guides, assessments, materials, and more to assist educators in teaching about the ethics of artificial intelligence. These activities were developed at the MIT Media Lab to meet a growing need for children to understand artificial intelligence, its impact on society, and how they might shape the future of AI. No code. Most are unplugged.||understand artificial intelligence, its impact on society, and how they might shape the future of AI||Grade 5-8||Encourages discussion on "Consider the impact of technology on the world.". Create "Ethical Matrix" to identify the stakeholders who care about a particular AI model, their values and where those values overlap or conflict.||adaptable||Andy|
|Interactive Muscial Experience||https://incredible-spinners.glitch.me/||Various Interactive visual activities using vision recognition AI to help students||Brenda|
|Book: You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place||https://www.janelleshane.com/book-you-look-like-a-thing||Through her hilarious experiments, real-world examples, and illuminating cartoons, she explains how AI understands our world, and what it gets wrong. More than just a working knowledge of AI, she hands readers the tools to be skeptical about claims of a smarter future.||Teachers reference, grades 9+||Andy|
|Vision recognition scavenger hunt||https://inventor.city/ai/vision||Learn how an AI vision recognition system works by finding its mistakes! Work together as a team to take pictures of common household items and see what the AI thinks they are. By examining and discussing the results, you will learn more about how AI makes decisions, and how society is impacted by errors.||Andy|
|Micro:bit of AI||https://ai-training.glitch.me/||This site will help bridge the gap between the Teachable Machine AI and a micro:bit giving you clever new ways to shape your projects. Train an AI to make a prediction using a library of data you give it, and then code your micro bit to use those predictions to activate motors, lights, and more! Simply click on "Pair Microbit" and follow the steps to get started today!||Andy|
|Pink Trombone: Bare-handed speech synthesis||https://dood.al/pinktrombone/||Web app that simulates the human speech biology. Change the palate, voicebox, lips, etc to manually make sounds||Andy|
|Elements of AI||https://www.elementsofai.com/||Kass|
|CS Unplugged: The Turing Test||https://classic.csunplugged.org/documents/activities/the-turing-test/unplugged-20-the_turing_test_0.pdf||Unplugged activity where learners try to differeniate between a computer and a human.||Kass|
|CS Unplugged: Intelligent Piece of Paper||https://classic.csunplugged.org/documents/activities/community-activities/artificial-intelligence/intelligentpaper.pdf||Unplugged activity where learners play tic-tac-toe against an "intelligent piece of paper"||Kass|
|Quick, Draw! (Google Experiment)||https://quickdraw.withgoogle.com/||Virtual pictionary game where a neural network tries to guess what you’re drawing||Kass|
|Tiny Sorter (Google Experiment)||https://experiments.withgoogle.com/tiny-sorter/view||Sort objects using a DIY Arduino sorting bot and the Teachable Machine.||Kass|
|Anti-Cyberbullying Assistant||https://www.canadalearningcode.ca/lessons/anti-cyberbullying-assistant||Free Canada Learning Code lesson plan where learners create an AI-powered assistant that will help them identify cyberbullying using Machine Learning for Kids||Kass|
|Learn Like a Computer||https://www.canadalearningcode.ca/lessons/learn-like-a-computer/||Unplugged activity where learners will explore how machine learning algorithms work, and their uses in day-to-day life.||Kass|
|Machine Zines||https://towardsdatascience.com/helping-kids-play-with-artificial-intelligence-68af8f8ba280||Create a paper zine with content inspired by an AI "muse"||Using the GPT-2 language model to generate text. Students and educators can create a text prompt together and put it in the GPT-2 text generator and it will continue writing text based on the prompt. Educators should screen generated text before students can see incase GPT-2 generates anything inappropriate. These AI generated texts can be used in papercraft zines for students to create and decorate for all occasions||papercraft, AI text generation||1-6||Training data||Visual arts, language||Kass|
|Machine Learning Basics||https://www.machinelearningbasic.com/||machine learning problems for K12.students. Twelve lesson problems on training artificial neural networks are included in the following website with python codes.||Containing 13 lessons, training various neural networks in image recognition, translation, and simple mathematics, these topics have a low bar of entry and can be inserted into many different curriculum. Delivered through short Youtube videos, these lessons require no coding - the neural network training is done through a GUI||machine learning||K-12||N/A||Problem Solving||Andy|
|Lois Lab Python coding||https://loislab.org||a series of lessons presented in an interactive learning environment where students develop an algorithm in Python that learns how to navigate a maze, and can then be applied to a whole class of problems (like playing tic-tac-toe) without any alteration to the algorithm.||The intent is to explore underlying concepts - guesses and observations, optimization, exploration & exploitation, etc - using problems with dimensions and constraints on a scale that doesn't require calculus as a prerequisite. Because they code the algorithm themselves in our environment using Jupyter notebooks, there are no "black boxes" - they are able to see all the steps and visualize the data.||Developing Python algorithms||K-12||N/A||Problem Solving||Andy|