AI Literacy Pathway (PK-13+)

Suggested topics, not active course modules. Choose a topic and adapt the examples below; this page does not enroll learners or save completion.

• What is AI? An age-appropriate introduction
• AI Helpers in Daily Life
• Digital Safety with AI Tools
• Creative AI Projects
• AI vs. Human Intelligence

Sort the helpers

Suggested time: 20–25 minutes

Learning goal: Distinguish people, simple tools, and tools that may use AI.

Materials: Paper, crayons, and drawings of a calculator, voice assistant, bicycle, and a person.

1. Draw or name familiar helpers together. For younger children, use pictures and oral answers.
2. Sort them into people and tools. Ask which tools respond to a question or suggest something.
3. Explain that some tools use patterns learned from examples. A tool can make mistakes and does not think or feel like a person.
4. Have the learner draw one way a person can check a tool's answer.

Discuss and reflect: Who could you ask if a computer tells you something surprising? Keep the drawing as an optional learning sample.

Pause and ask a trusted adult

Suggested time: 10–15 minutes

Learning goal: Practice asking for help without sharing private information.

Materials: Paper and two fictional scenarios: a tool asks for your address; a tool gives a confusing answer.

1. Read one scenario aloud. Ask how the learner might feel.
2. Role-play pausing, closing the tool, and asking a trusted adult.
3. Draw a reminder card: do not share your name, address, or school with an unfamiliar tool.

Discuss and reflect: Name a trusted adult you could ask. No child needs an AI account to complete this example.

Suggested topics, not active course modules. Choose a topic and adapt the examples below; this page does not enroll learners or save completion.

• Using AI Writing Assistants Effectively
• AI Image and Video Generation
• Data Privacy and AI Ethics
• Evaluating AI-Generated Content
• Creating with AI: Music, Art, Code

Fact-check a fictional AI answer

Suggested time: 30–40 minutes

Learning goal: Use evidence rather than fluent wording to judge a claim.

Materials: A notebook, reference books or trusted educational sources, and the example claim below.

1. Use this intentionally incorrect example, not a live AI result: 'All planets in our solar system have solid surfaces.'
2. Find two reliable sources about gas giants and terrestrial planets. Record titles or URLs.
3. Mark which part of the example is unsupported. Rewrite it using evidence from your sources.
4. Discuss why a confident answer can still be wrong and when to seek an expert's help.

Discuss and reflect: What evidence changed your answer? Save your corrected paragraph and source notes if useful.

Who gets left out?

Suggested time: 20–25 minutes

Learning goal: Recognize that a simple rule can exclude useful perspectives.

Materials: Paper and a fictional recommendation rule: suggest a book only if it has over 100 reviews.

1. List whose books might be excluded by the rule, including new authors or less widely spoken languages.
2. Take turns explaining the rule from a reader's and an author's perspective.
3. Propose a fairer rule and describe one trade-off.

Discuss and reflect: How could a human reviewer spot an unfair recommendation? Discuss the example without using classmates' personal data.

Suggested topics, not active course modules. Choose a topic and adapt the examples below; this page does not enroll learners or save completion.

• Machine Learning Fundamentals
• AI Bias and Ethical Decision Making
• Building Simple AI Applications
• AI in Career Fields
• Leading AI Innovation Projects

Test a paper classifier

Suggested time: 45–60 minutes

Learning goal: Explain training examples, held-out tests, and limits of a classification rule.

Materials: Index cards, a ruler, and drawn shapes. No coding or personal dataset required.

1. Draw 12 varied shapes. Label each as curved or straight-edged, including ambiguous examples.
2. Use eight cards to develop a classification rule. Set the remaining four aside before refining it.
3. Apply the rule to the held-out cards. Record each prediction and whether it matches your label.
4. Describe failures and ambiguity. Explain why four test examples do not establish real-world reliability.
5. Write a short model card: purpose, examples used, limitations, and situations where a person should decide.

Discuss and reflect: How would a different set of examples change the rule? Keep the model card as an optional portfolio artifact.

A human appeal matters

Suggested time: 30–40 minutes

Learning goal: Consider accountability, empathy, and meaningful human review.

Materials: A fictional scenario: an automated system rejects a scholarship application without an explanation.

1. Assign fictional roles: applicant, reviewer, and system designer. Do not use real student records.
2. Identify what each person needs to understand the decision and challenge an error.
3. Draft an appeal process that protects privacy and includes a human decision-maker.
4. Discuss which decisions should not be left to automation alone.

Discuss and reflect: How would you know the appeal process is understandable and fair? Record your reasoning, not a simulated completion score.