Learn about Artificial Intelligence (AI)

AI and Machine Learning impact our entire world, changing how we live and how we work. That's why it’s critical for all of us to understand this increasingly important technology, including not just how it’s designed and applied, but also its societal and ethical implications.

Join us to explore AI in a new video series, train AI for Oceans in 25+ languages, discuss ethics, and more!


AI for Oceans

Help A.I. clean the oceans by training it to detect trash! Learn about training data and bias, and how AI can address world problems. View lesson plan.

How AI Works

Learn about how AI works and why it matters with this series of short videos. Featuring Microsoft CEO Satya Nadella and a diverse cast of experts.

AI and Ethics

Students reflect on the ethical implications of AI, then work together to create an “AI Code of Ethics” resource for AI creators and legislators everywhere.

In partnership with  
We thank Microsoft for supporting our vision and mission to ensure every child has the opportunity to learn computer science and the skills to succeed in the 21st century.

How AI Works

With an introduction by Microsoft CEO Satya Nadella, this series of short videos will introduce you to how artificial intelligence works and why it matters. Learn about neural networks, or how AI learns, and delve into issues like algorithmic bias and the ethics of AI decision-making.

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Exploring the Ethics of AI panel discussion

Go deeper with some of our favorite AI experts! This panel discussion touches on important issues like algorithmic bias and the future of work. Pair it with our AI & Ethics lesson plan for a great introduction to the ethics of artificial intelligence!


Videos about AI from other organizations:



More resources

CS for Good

Resources to inspire students to think deeply about the role computer science can play in creating a more equitable and sustainable world.

Imagine Cup Junior

This global AI for Good challenge introduces students to Microsoft’s AI for Good initiatives, empowering them to solve a problem in the world with the power of AI.


Activities Powered by AI and ML

Activity Description
Seeing AI A free app that narrates the world around you in a variety of languages.
Quick, Draw! Can a neural network learn to recognize doodling?
Teachable Machine Train a computer to recognize your own images, sounds, and poses.
AI Experiments with Google Start exploring machine learning through pictures, drawings, language, music, and more.
Zooniverse - Snapshot Mountain Zebra Help protect the endangered Cape Mountain Zebra by identifying the different animals in the images.


Teach and Learn about AI

Name Description Audience
Minecraft AI for Good Access free resources including a lesson plan, videos, computer science curriculum, and teacher trainings. (Requires Minecraft: Education Edition) K-12 (Reading required)
Free virtual workshops from Microsoft Spark curiosity with free STEM and coding workshops. K-12
Machine Learning for Kids Train a machine learning model with text, numbers, or images, and use it to make games in Scratch. K-12 (Reading required)
IBM: Machine Learning for Kids IBM: Machine Learning for Kids K-12 (Reading required)
ECS: Artificial Intelligence (AI) A new alternate curriculum unit for the Exploring Computer Science (ECS) curriculum. High school
Elements of AI A series of free online courses created by Reaktor and the University of Helsinki. High school
Cognimates An AI education platform for building games, programming robots and training. K-12 (Reading required)



AI for Oceans: Behind the Scenes

Levels 2-4 use a pretrained model provided by the TensorFlow MobileNet project. A MobileNet model is a convolutional neural network that has been trained on ImageNet, a dataset of over 14 million images hand-annotated with words such as "balloon" or "strawberry". In order to customize this model with the labeled training data the student generates in this activity, we use a technique called Transfer Learning. Each image in the training dataset is fed to MobileNet, as pixels, to obtain a list of annotations that are most likely to apply to it. Then, for a new image, we feed it to MobileNet and compare its resulting list of annotations to those from the training dataset. We classify the new image with the same label (such as "fish" or "not fish") as the images from the training set with the most similar results.

Levels 6-8 use a Support-Vector Machine (SVM). We look at each component of the fish (such as eyes, mouth, body) and assemble all of the metadata for the components (such as number of teeth, body shape) into a vector of numbers for each fish. We use these vectors to train the SVM. Based on the training data, the SVM separates the "space" of all possible fish into two parts, which correspond to the classes we are trying to learn (such as "blue" or "not blue").

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