What Is Artificial Intelligence?
A plain-language introduction to what AI is, what it is not, and why it matters.
AI in Plain Language
Learning objective
By the end of this lesson, the learner will be able to explain ai in plain language, apply it to a realistic situation, and defend the chosen action with evidence.
Core lesson
AI is software that performs tasks associated with human intelligence, such as recognizing patterns, generating language, predicting outcomes, and supporting decisions.
Instructor teaching guidance
Begin by establishing what the learner already knows about ai in plain language. Teach the principle in plain language, define unfamiliar terms, then connect it to the course goal. Ask the learner to explain the idea back in their own words before moving to application.
Applied exercise
List three AI systems you encountered today.
Facilitation sequence
Guide the learner through the application in five steps: describe the present situation, identify the lesson principle, choose an action, name the evidence to review, and define how success will be measured. Use follow-up questions when an answer is vague or unsupported.
Mastery standard
AI does not think or feel exactly as a person does.
Knowledge check
Ask the learner to explain why “AI does not think or feel exactly as a person does.” is the correct standard, then identify one limitation, one risk, and one piece of evidence that could change the answer. Do not mark mastery from confidence alone; require clear reasoning and practical application.
How Machines Learn
Learning objective
By the end of this lesson, the learner will be able to explain how machines learn, apply it to a realistic situation, and defend the chosen action with evidence.
Core lesson
Machine learning finds patterns in examples rather than following only hand-written rules.
Instructor teaching guidance
Begin by establishing what the learner already knows about how machines learn. Teach the principle in plain language, define unfamiliar terms, then connect it to the course goal. Ask the learner to explain the idea back in their own words before moving to application.
Applied exercise
Explain training data using a student-and-textbook analogy.
Facilitation sequence
Guide the learner through the application in five steps: describe the present situation, identify the lesson principle, choose an action, name the evidence to review, and define how success will be measured. Use follow-up questions when an answer is vague or unsupported.
Mastery standard
Training teaches patterns; it does not guarantee truth.
Knowledge check
Ask the learner to explain why “Training teaches patterns; it does not guarantee truth.” is the correct standard, then identify one limitation, one risk, and one piece of evidence that could change the answer. Do not mark mastery from confidence alone; require clear reasoning and practical application.
Generative AI
Learning objective
By the end of this lesson, the learner will be able to explain generative ai, apply it to a realistic situation, and defend the chosen action with evidence.
Core lesson
Generative systems produce new text, images, audio, video, or code based on learned patterns.
Instructor teaching guidance
Begin by establishing what the learner already knows about generative ai. Teach the principle in plain language, define unfamiliar terms, then connect it to the course goal. Ask the learner to explain the idea back in their own words before moving to application.
Applied exercise
Compare a search result with a generated answer.
Facilitation sequence
Guide the learner through the application in five steps: describe the present situation, identify the lesson principle, choose an action, name the evidence to review, and define how success will be measured. Use follow-up questions when an answer is vague or unsupported.
Mastery standard
Generation predicts useful output; it does not retrieve perfect facts automatically.
Knowledge check
Ask the learner to explain why “Generation predicts useful output; it does not retrieve perfect facts automatically.” is the correct standard, then identify one limitation, one risk, and one piece of evidence that could change the answer. Do not mark mastery from confidence alone; require clear reasoning and practical application.
AI, Automation, and Algorithms
Learning objective
By the end of this lesson, the learner will be able to explain ai, automation, and algorithms, apply it to a realistic situation, and defend the chosen action with evidence.
Core lesson
Automation follows a workflow, algorithms are procedures, and AI adapts or generates from patterns.
Instructor teaching guidance
Begin by establishing what the learner already knows about ai, automation, and algorithms. Teach the principle in plain language, define unfamiliar terms, then connect it to the course goal. Ask the learner to explain the idea back in their own words before moving to application.
Applied exercise
Classify a thermostat, spreadsheet formula, chatbot, and recommendation engine.
Facilitation sequence
Guide the learner through the application in five steps: describe the present situation, identify the lesson principle, choose an action, name the evidence to review, and define how success will be measured. Use follow-up questions when an answer is vague or unsupported.
Mastery standard
Not every automated system is AI.
Knowledge check
Ask the learner to explain why “Not every automated system is AI.” is the correct standard, then identify one limitation, one risk, and one piece of evidence that could change the answer. Do not mark mastery from confidence alone; require clear reasoning and practical application.
Strengths and Limits
Learning objective
By the end of this lesson, the learner will be able to explain strengths and limits, apply it to a realistic situation, and defend the chosen action with evidence.
Core lesson
AI can scale analysis and drafting, but it can hallucinate, inherit bias, and miss context.
Instructor teaching guidance
Begin by establishing what the learner already knows about strengths and limits. Teach the principle in plain language, define unfamiliar terms, then connect it to the course goal. Ask the learner to explain the idea back in their own words before moving to application.
Applied exercise
Name one task AI should assist and one it should not decide alone.
Facilitation sequence
Guide the learner through the application in five steps: describe the present situation, identify the lesson principle, choose an action, name the evidence to review, and define how success will be measured. Use follow-up questions when an answer is vague or unsupported.
Mastery standard
Human review is essential when consequences are high.
Knowledge check
Ask the learner to explain why “Human review is essential when consequences are high.” is the correct standard, then identify one limitation, one risk, and one piece of evidence that could change the answer. Do not mark mastery from confidence alone; require clear reasoning and practical application.
Why AI Matters Now
Learning objective
By the end of this lesson, the learner will be able to explain why ai matters now, apply it to a realistic situation, and defend the chosen action with evidence.
Core lesson
Better models, data, computing, interfaces, and investment have made AI broadly usable.
Instructor teaching guidance
Begin by establishing what the learner already knows about why ai matters now. Teach the principle in plain language, define unfamiliar terms, then connect it to the course goal. Ask the learner to explain the idea back in their own words before moving to application.
Applied exercise
Describe one opportunity AI creates for your family or organization.
Facilitation sequence
Guide the learner through the application in five steps: describe the present situation, identify the lesson principle, choose an action, name the evidence to review, and define how success will be measured. Use follow-up questions when an answer is vague or unsupported.
Mastery standard
AI literacy is becoming a basic leadership capability.
Knowledge check
Ask the learner to explain why “AI literacy is becoming a basic leadership capability.” is the correct standard, then identify one limitation, one risk, and one piece of evidence that could change the answer. Do not mark mastery from confidence alone; require clear reasoning and practical application.