ORA Intelligent Currency Challenge
Attachments: ORA Intelligent Currency Challenge Student GuideORA Intelligent Currency Challenge Teacher GuideORA Intelligent Currency Challenge PrintablesORA Intelligent Currency Challenge BlocksORA Intelligent Currency Challenge – Time-Lapse VideoORA Intelligent Currency Challenge – First Three Coins More Properties: Grades: 10, 11, 12, 9 Subjects: AI, Computer Science, Engineering/Tech Academic Standards: CSTA.2-AP-11, CSTA.2-AP-12, CSTA.2-AP-13, CSTA.2-AP-19, CSTA.2-CS-02, CSTA.3A-AP-17, CSTA.3A-DA-11, CSTA.3B-AP-08, […]
ORA AI-Guided Sorting and Coordinate Transformation Challenge
Attachments: ORA AI-Guided Sorting and Coordinate Transformation Challenge Student GuideORA AI-Guided Sorting and Coordinate Transformation Challenge Teacher GuideORA AI-Guided Sorting and Coordinate Transformation PrintablesORA AI-Guided Sorting and Coordinate Transformation BlocksORA AI-Guided Sorting and Coordinate Transformation Time-Lapse Video More Properties: Grades: 10, 11, 12, 9 Subjects: AI, Computer Science, Engineering/Tech Academic Standards: CSTA.2-AP-11, CSTA.2-AP-12, CSTA.2-AP-13, CSTA.2-AP-19, […]
Invisible Inputs: Analyzing Bias in AI Healthcare Tools
Students investigate how biased proxy data led a healthcare algorithm to underestimate the needs of certain patient groups. They analyze the case, debate fairness, and use Color Codes to model how design choices affect equity in AI.
Elective Algorithm Design
Students compare their own class scheduling decisions to those made by AI to identify possible bias in the AI algorithm. Then, they design ethical decision trees and use Ozobot Color Codes to model fair and equitable outcomes.
Ethics Lab: AI on Trial
Step into the role of an AI Ethicist! Students will investigate how AI can make biased decisions, propose improvements, and use Color Codes to demonstrate their evaluation of AI decisions with Ozobot.
Think Like a Model: Large Language Model
Students will run a program to simulate training an LLM and make a prediction about the next color. Students will write the beginning of a short story, then exchange with a partner who will finish the story and design a pathway for Ozobot to follow.
Prediction Pro
Students explore how LLMs learn by predicting Ozobot’s next action. Then, programming Ozobots to follow patterns and act out story paths, connecting repetition and prediction to the way language models are trained.
Patterns Not Memory: How LLMs Work
Students use skip counting as a way to identify patterns and make predictions. Then, they program Ozobot to complete a pattern to understand how LLMs work.
Making Decisions with Computer Vision
See Ozobot think on its wheels with computer vision! Connect a camera, ask the LLM your question, and watch as your bot zips off in the correct direction.
Digital Zoologists: Researching with the LLM
Become a digital zoologist by using the LLM block to research animals and create a field report! Test what the LLM can accurately answer by asking a variety of questions with different output types to program Ozobot’s behavior based on the responses.