Privacy & Legal
Our new curriculum module focuses on AI ethics, examines issues of bias, and explores and explains fundamental concepts through a number of online and unplugged activities and full-group discussions.
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!
The AI and Machine Learning Module is roughly a five week curriculum module that can be taught as a standalone module or as an optional unit in CS Discoveries. It focuses on AI ethics, examines issues of bias, and explores and explains fundamental concepts
Because machine learning depends on large sets of data, the new unit includes real life datasets on healthcare, demographics, and more to engage students while exploring questions like, “What is a problem Machine Learning can help solve?” “How can AI help society?” “Who is benefiting from AI?” “Who is being harmed?” “Who is involved?” “Who is missing?”
Ethical considerations will be at the forefront of these discussions, with frequent discussion points and lessons around the impacts of these technologies. This will help students develop a holistic, thoughtful understanding of these technologies while they learn the technical underpinnings of how the technologies work.
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.
|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.|
|Akinator||Think of a person, even from a book or movie, and this app will guess who you’re thinking of by asking questions.|
|Survival of the Best Fit||An online game where you use machine learning to help screen candidates for job interviews. In the process, you uncover how bias can creep into AI applications and see the impact it has on the people involved.|
|ML Playground||An interactive demo of several common machine learning algorithms, with links to additional resources to keep exploring.|
|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 (Reading required)|
|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)|
|AI4All: Open Learning Curriculum||An interdisciplinary, adaptable curriculum to support high school teachers and students exploring AI. Also includes the ‘Bytes of AI’ series, which are smaller lessons that can be incorporated in any classroom.||High School Teachers|
|MIT: AI + Ethics Curriculum||A middle-school, project-based curriculum that explores issues of ethics and societal impact of AI.||Middle School Teachers|
|MIT: Dancing with AI||A project-based curriculum about making interactive, movement-focused AI projects. All projects are completed in Scratch using new AI blocks to detect body and facial movements.||Middle School Teachers|
|IBM: AI Foundations Self-Paced Course||A beginner-friendly self-paced introduction to AI designed for high school students. Students learn the foundations of AI and earn a badge at the end of the course.||High School Students|
|ActuaAI: AI Activities||A series of AI-related activities that can be completed at-home or as part of an after-school program.||K-12 Students, Teachers or Parents|
|ECS: Artificial Intelligence (AI)||A new alternate curriculum unit for the Exploring Computer Science (ECS) curriculum.||Middle and High School Teachers|
|ISTE: AI in Education||A collection of resources for teaching AI in the classroom for all grade levels. Includes links to projects and activities, as well as teacher professional development.||K-12 Teachers|
|AI for Teachers||A collection of resources, activities, lesson plans, and professional development for implementing AI in your classroom.||K-12 Teachers|
|AI4K12 Resources||A list of resources from the AI4K12 Working Group for teachers interested in bringing AI into their classroom.||K-12 Teachers|
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").