Idaho Department of Education · Draft June 2026
Idaho AI Classroom
Classroom-ready lessons for the 2026 Idaho 9–12 Artificial Intelligence Content Standards.
Forty-two full lesson plans — one per standard — with targets, sequence, hands-on work, differentiation, assessment, and FERPA-aware responsible use. Open or download a single HTML file that works offline on a Chromebook, USB drive, or school shared folder.
- Lessons
- 42
- Domains
- 3
- Core period
- 50 min
AI Fluency
Recommended 9–10 · 16 lessons
Introductory competencies for navigating, using, and critically evaluating AI in academic and professional settings. Students learn when AI belongs in a task, how to read its outputs, and how to keep human judgment in charge.
Appropriate Use
Decide when AI is the right tool, when it is not, and how over-reliance can reshape thinking and feeling.
The Right Tool for the Job
Understand when AI versus non-AI solutions are appropriate.
What We Lose When We Hand It Over
Recognize that some AI can handle cognitive tasks, but reliance on AI might diminish individual human cognitive and emotional development.
Real Ground, Simulated Sky
Explain the difference between physical and simulated environments.
Not All Models Speak
Understand that Large Language Models (LLM) are just one type of AI model and that others exist with different purposes.
College and Career Readiness
Interpret AI output for real audiences and connect AI skills to personal goals and Idaho career pathways.
Translate the Machine
Interpret AI output and be able to communicate those to a diverse audience.
Idaho Futures, AI Skills
Connect AI knowledge and skills needed to achieve students' personal goals and career aspirations.
Evaluation and Critical Thinking
Question what AI is, how it learns from patterns, where it fails, and how agents complete defined tasks.
Minds, Machines, and Responsibility
Debate perspectives on differences between human and artificial intelligence and their implications for consciousness, creativity, ethics, and human responsibility.
Patterns In, Patterns Out
Recognize and understand that AI systems are a technology that uses patterns in data to make decisions or generate new things.
Bias, Limits, and Blind Spots
Analyze the potential biases and limitations of AI output.
The Dataset Decides
Analyze how choice of data sets used to train AI models can lead to potential bias in the output.
Catching Hallucinations
Recognize when an AI model produces erroneous outputs (i.e., hallucinations).
Senses vs. Statistics
Give examples of how humans combine information from different bodily sensory inputs to make sense of the world, and compare that to how AI models learn about the world.
What an Agent Actually Does
Recognize and understand how AI agents can accomplish a defined task.
Application
Use AI to critique work, generate simple code, and construct agents that accomplish a defined task.
A Second Reader, Not a Ghostwriter
Use an AI tool to critique student work.
Run It, Don't Worship It
Use AI tools to generate code for a simple coding project.
Build a Narrow Agent
Construct AI agents to accomplish a defined task.
AI Impact
Recommended 11–12 · 10 lessons
Higher-level analysis of how generative AI has changed learning, work, ethics, privacy, and civic life. Students debate regulation, evaluate harms, and practice informed decision-making.
Ethics and Privacy
Weigh GenAI’s ethical shift, accessibility, large-scale data collection, unauthorized training data, and cybersecurity.
After the Flood: GenAI in Every Subject
Evaluate the ethical implications of how AI has changed since GenAI has become commonplace.
Who Gets a Better Door
Analyze how AI tools shape user experiences for people with diverse backgrounds and characteristics.
The Cost of a Billion Examples
Explore the societal, environmental, and ethical implications of large-scale data collection and processing and how this relates to AI applications.
Used Without Asking
Assess how unauthorized data collection has influenced the practice of training AI models.
Defense, Offense, and Dual Use
Analyze the benefits, risks, and ethical implications of AI in cybersecurity.
Impact on Society
Evaluate outputs for harm, study AI in real careers, trace bias through data decisions, and examine long-term human choices.
Three Tests: Bias, Accuracy, Harm
Evaluate AI-generated output to assess bias, accuracy, and potential harms.
Idaho Work, AI Tools
Analyze how workers in different careers use AI to solve problems.
Bias Enters at Every Stage
Analyze how decisions made at different stages of working with data can lead to biased data, misleading conclusions, and compromised AI models.
The Hard Debate, Senior Year
Debate perspectives on differences between human and artificial intelligence and their implications for consciousness, creativity, ethics, and human responsibility.
Who Decides, Who Pays
Evaluate how human choices in using, designing, deploying, and regulating AI technologies have risks, benefits, and long-term impacts.
AI Technical Aptitude
Recommended 9–12 CS / engineering · 16 lessons
Building, not just using, AI: data collection and cleaning, model choice, training workflow, metrics, and safeguards. Intended for computer science and other engineering pathways.
Data and Analysis
Clean, verify, acquire, and critique data; justify model selection; reduce bias by improving examples and non-examples.
Make the Text Behave
Use a digital tool to clean and organize text-based data.
When a Number Is a Word
Evaluate different approaches to verifying consistency and compliance with expected data types, values, and ranges.
Collect, Split, Then Train
Examine the procedure of how data is used to train machine learning models.
From Web Page to Table
Apply data acquisition, cleaning, and transformation techniques to prepare data for AI analysis.
Better Examples, Fairer Model
Investigate ways to improve the accuracy of a machine learning model and reduce bias by refining the quality of examples and nonexamples in the training data.
Pick the Model That Fits
Justify the selection of a type of machine learning model to accomplish a task.
Critique the Corpus
Evaluate training data by examining its source, quality, representativeness, potential biases, and privacy implications.
AI Methods
Use prebuilt models and agents, compare ML families, plan human-in-the-loop safeguards, and communicate metrics honestly.
Call a Model, Don't Train One
Create an application using pre-existing supervised learning models to make predictions or classifications.
Drop In an Agent
Integrate a prebuilt AI agent into an application.
Data Writes the Function
Discuss how a machine learning model generates classifications or predictions.
Rules, Data, or Both
Justify whether a problem is best solved using procedural instructions, rule-based logic, data-driven methods, or a combination of these approaches.
Three Families, Three Jobs
Understand the differences between common machine learning models.
What This Model Cannot Do
Evaluate the features and limitations of a machine learning model.
Human in the Loop, On Purpose
Plan safeguards for AI systems that protect human well-being and privacy while ensuring meaningful human involvement in decision-making.
The Lifecycle, Methods View
Examine the procedure of how data is used to train machine learning models.
Accuracy Is a Trap
Interpret AI results using common metrics and be able to communicate those to a diverse audience.
Need a year at a glance?
Nine units from fluency through technical aptitude.