AI Literacy Lab

Open-source AI literacy curriculum

Build judgment, not just awareness.

AI Literacy Lab is a free curriculum for helping people make better decisions when AI systems are useful, persuasive, incomplete, risky, or wrong.

Start Here Project thesis
Open-source No login Local-only learner writing Source-informed

Standards-aware practice

Informed by risk management, privacy, and learning science.

The lab is designed for general office work, including regulated settings where people need practical habits: classify risk, protect data, verify claims, preserve accountability, and escalate when the use exceeds ordinary review.

Self-check prompts run in the browser and do not submit learner writing. They support practice; they do not certify correctness or policy compliance.

Syllabus

A sequenced lab path

Move through the modules in order. Progress is stored only in this browser, so the sequence supports learning without accounts or tracking.

The lab begins with a pre-reflection and ends with a post-reflection. Both are saved locally and included on the learning record, so write them as real evidence of learning rather than placeholders.

What completion means

  1. Write the opening reflection.
  2. Commit to a judgment before the fuller context appears.
  3. Reconsider the case using the Before You Act prompts.
  4. Document a short judgment or artifact.
  5. Reveal the review and mark the module complete.

Estimated full lab time: 2-3 hours.

Your progress stays in this browser unless you reset it, clear site data, use private browsing, switch browser or device, or access the lab from a different domain.

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  1. Module 1

    What AI Is Good and Bad At

    15 min

    Learners classify AI uses by considering task type, context, stakes, data sensitivity, verification options, and accountability.

    Open
  2. Module 2

    Why Confident Answers Can Be Wrong

    20 min

    Learners inspect polished AI output for unsupported claims, missing evidence, false precision, and overconfident language.

    Locked
  3. Module 3

    Data, Privacy, and Confidentiality

    20 min

    Learners classify information sensitivity and choose safer alternatives for AI-assisted work.

    Locked
  4. Module 4

    Bias, Fairness, and Representational Harm

    25 min

    Learners examine neutral-looking outputs for uneven assumptions, proxy variables, and downstream harm.

    Locked
  5. Module 5

    Human Accountability and Review

    20 min

    Learners distinguish AI assistance from delegated responsibility and define meaningful human review.

    Locked
  6. Module 6

    Using AI Well in Everyday Work

    20 min

    Learners redesign AI-assisted workflows and practice a speed-versus-verification judgment under workplace pressure.

    Locked
  7. Module 7

    Risk Classification and Escalation

    25 min

    Learners classify ambiguous AI use cases and defend whether to proceed, modify, pause, document, or escalate.

    Locked
  8. Module 8

    Public Benefits AI Decision Simulation

    35 min

    Learners integrate the full curriculum into a defensible recommendation for a public benefits backlog scenario.

    Locked