AI 101: Practical Foundations
Learn the vocabulary, systems, risks, and practical choices behind modern AI.
The AI Landscape
Learning objective
By the end of this lesson, the learner will be able to explain the ai landscape, apply it to a realistic situation, and defend the chosen action with evidence.
Core lesson
AI includes machine learning, deep learning, language models, vision systems, robotics, and decision support.
Instructor teaching guidance
Begin by establishing what the learner already knows about the ai landscape. 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
Create a one-page map of the AI tools you use.
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
Choose tools based on the job, not the hype.
Knowledge check
Ask the learner to explain why “Choose tools based on the job, not the hype.” 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.
Models, Data, and Compute
Learning objective
By the end of this lesson, the learner will be able to explain models, data, and compute, apply it to a realistic situation, and defend the chosen action with evidence.
Core lesson
Models learn patterns from data using compute; capability depends on all three.
Instructor teaching guidance
Begin by establishing what the learner already knows about models, data, and compute. 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 model, data, and compute in one minute.
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
A large model is not automatically the right model.
Knowledge check
Ask the learner to explain why “A large model is not automatically the right model.” 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.
Language Models
Learning objective
By the end of this lesson, the learner will be able to explain language models, apply it to a realistic situation, and defend the chosen action with evidence.
Core lesson
Language models predict and generate sequences of language using context.
Instructor teaching guidance
Begin by establishing what the learner already knows about language models. 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
Ask the same question three different ways and compare results.
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
Outputs are probabilistic, not guaranteed.
Knowledge check
Ask the learner to explain why “Outputs are probabilistic, not guaranteed.” 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.
Multimodal AI
Learning objective
By the end of this lesson, the learner will be able to explain multimodal ai, apply it to a realistic situation, and defend the chosen action with evidence.
Core lesson
Modern systems can work across text, images, audio, video, and structured data.
Instructor teaching guidance
Begin by establishing what the learner already knows about multimodal 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
Design a workflow combining a photo and written instructions.
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
Use the modality that carries the best evidence.
Knowledge check
Ask the learner to explain why “Use the modality that carries the best evidence.” 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.
Accuracy and Verification
Learning objective
By the end of this lesson, the learner will be able to explain accuracy and verification, apply it to a realistic situation, and defend the chosen action with evidence.
Core lesson
Grounding, citations, tests, and human review reduce error.
Instructor teaching guidance
Begin by establishing what the learner already knows about accuracy and verification. 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
Create a verification checklist for an AI 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
Fluent language is not proof of accuracy.
Knowledge check
Ask the learner to explain why “Fluent language is not proof of accuracy.” 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.
Bias, Privacy, and Safety
Learning objective
By the end of this lesson, the learner will be able to explain bias, privacy, and safety, apply it to a realistic situation, and defend the chosen action with evidence.
Core lesson
Data and design choices can produce unfairness, leakage, or harmful recommendations.
Instructor teaching guidance
Begin by establishing what the learner already knows about bias, privacy, and safety. 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
Identify sensitive information that should not enter a public model.
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
Protect people before optimizing convenience.
Knowledge check
Ask the learner to explain why “Protect people before optimizing convenience.” 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.
Choosing an AI Tool
Learning objective
By the end of this lesson, the learner will be able to explain choosing an ai tool, apply it to a realistic situation, and defend the chosen action with evidence.
Core lesson
Evaluate purpose, accuracy, privacy, integrations, cost, and governance.
Instructor teaching guidance
Begin by establishing what the learner already knows about choosing an ai tool. 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
Score two tools against the same use case.
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
Start with requirements, then select technology.
Knowledge check
Ask the learner to explain why “Start with requirements, then select technology.” 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.
Your First AI Workflow
Learning objective
By the end of this lesson, the learner will be able to explain your first ai workflow, apply it to a realistic situation, and defend the chosen action with evidence.
Core lesson
Combine a clear input, AI task, human review, and measurable outcome.
Instructor teaching guidance
Begin by establishing what the learner already knows about your first ai workflow. 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
Design one repeatable weekly AI workflow.
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
A workflow creates value when it reliably changes an outcome.
Knowledge check
Ask the learner to explain why “A workflow creates value when it reliably changes an outcome.” 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.