Tool Selection:
COSN and CAST: AI and Accessibility in Education
Future of Privacy Forum: Vetting Generative AI Tools for Use in Schools
Training For Educators:
Code.org: AI 101 for Teachers
Common Sense Education
Classroom Resources:
Common Sense Education: AI Literacy Lessons for Grades 6–12
Code.org: Curricular materials and educator training modules
AiEDU: Intro to Artificial Intelligence (Grades 9–12) , and AI Snapshots
AI for Education:
Hour of AI Activities
https://csforall.org/en-US/activities/hour-of-ai
MIT RAISE Playground
https://playground.raise.mit.edu/
Pictoblox.ai
https://pictoblox.ai/
https://ai.thestempedia.com/docs/pictoblox/
Teachable Machine
https://teachablemachine.withgoogle.com/
Teaching artificial intelligence to elementary students works best when abstract concepts are turned into physical, unplugged games and hands-on experiments.
Kindergarten – 1st Grade: Pattern Recognition & Machine Rules
"Is It a Robot?" Sorting Game: Kids sort physical objects or picture cards into two categories: Needs a Human vs. Can Be Done by a Computer. This introduces the distinction between human intelligence/emotion and automated tasks.
Human Algorithm Maze: One student acts as the "Robot" while another acts as the "Coder." The Coder can only use four specific directional command cards (Up, Down, Left, Right) to guide the Robot across a grid on the floor.
2nd Grade – 3rd Grade: Training Data & Computer Vision
Train a "Quick Draw" Model: Using online tools like Google’s Quick, Draw!(https://quickdraw.withgoogle.com/ ), students draw objects while an AI guesses what they are drawing.
Discussion: How did the computer know that three connected lines make a triangle?
The "Good Data / Bad Data" Mystery: Pass around two mystery boxes filled with mystery items. Box A has 20 different plastic animals; Box B has 19 plastic farm animals and 1 lion. Ask students to predict what animal someone will draw next from each box to demonstrate how training data shapes an AI's predictions.
4th Grade – 5th Grade: Machine Learning & AI Ethics
Teachable Machine Experiments: Using Google's Teachable Machine (https://teachablemachine.withgoogle.com/ ) (webcam-based), students train their own simple model to recognize classroom items, hand gestures, or facial expressions.
AI Bias & "Train the Sorting Bot": Give students a stack of images (e.g., shoe styles) and have them "train" a partner using strict rules (e.g., "Only classify shoes with laces as running shoes"). Have them test what happens when a slip-on running shoe appears, highlighting how rigid or incomplete data creates unintended mistakes.
Generative Art & Prompt Crafting: Show students a generative image created from a prompt. Have them work backward to guess the exact keywords used, then experiment with changing specific adjectives to see how output varies.
Key Concepts Covered Across Grades
Algorithms: Instructions given step-by-step.
Training Data: The examples a machine learns from.
Pattern Recognition: Finding similarities in shapes, numbers, or actions.
Bias & Limitations: AI makes mistakes when its instructions or data are flawed.