{"name":"AI Understanding Public AI Literacy Curriculum","version":"1.0.0","publishedAt":"2026-08-27","canonicalUrl":"https://aiunderstanding.org/research/ai-literacy-curriculum","license":{"name":"Creative Commons Attribution 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"methodology":{"purpose":"A public, competency-mapped curriculum for practical and responsible AI literacy.","structure":"Five courses map learning objectives, practice tasks, and applied capstones to a shared competency framework.","limitations":"The framework is an educational design resource. Completion and learning-outcome benchmarks require longitudinal learner data and are not claimed by this release."},"competencies":[{"key":"ai-concepts","label":"AI concepts and boundaries"},{"key":"data-training","label":"Data, training, and generalization"},{"key":"model-inference","label":"Models, inference, and limitations"},{"key":"evaluation","label":"Evaluation and benchmark literacy"},{"key":"uncertainty","label":"Uncertainty and confidence calibration"},{"key":"source-verification","label":"Source and claim verification"},{"key":"prompt-design","label":"Prompt and context design"},{"key":"workflow-design","label":"AI-assisted workflow design"},{"key":"output-review","label":"Output review and error detection"},{"key":"tool-selection","label":"Tool selection and comparison"},{"key":"automation-safety","label":"Automation boundaries and safeguards"},{"key":"privacy","label":"Privacy and data handling"},{"key":"bias-fairness","label":"Bias and fairness"},{"key":"copyright","label":"Copyright and content provenance"},{"key":"security","label":"AI security and misuse risk"},{"key":"governance","label":"Governance and accountability"},{"key":"policy","label":"AI policy and regulation"},{"key":"workforce","label":"Workforce and job impacts"},{"key":"llm-mechanics","label":"Language-model mechanics"},{"key":"rag","label":"Retrieval-augmented generation"},{"key":"agents","label":"Agents, tools, and long-running tasks"},{"key":"multimodal","label":"Multimodal AI systems"},{"key":"model-cost","label":"Model cost and operational tradeoffs"},{"key":"experiment-design","label":"Experiment and pilot design"},{"key":"metrics","label":"Success metrics and monitoring"},{"key":"incident-response","label":"AI incident response"},{"key":"stakeholder-communication","label":"Stakeholder communication"},{"key":"public-interest","label":"Public-interest impact analysis"},{"key":"career-readiness","label":"AI career readiness"},{"key":"continuous-learning","label":"Continuous AI learning"}],"courses":[{"slug":"ai-literacy-foundations","title":"AI Foundations","audience":"Anyone who wants a reliable mental model of modern AI","description":"Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.","level":"Beginner","estimatedHours":4,"prerequisites":[],"objectives":["Explain training and inference in plain language.","Separate demonstrated capability from marketing claims.","Evaluate AI outputs using evidence, uncertainty, and fit-for-purpose tests."],"modules":[{"title":"What AI is","outcome":"Distinguish AI, machine learning, and ordinary software.","guideSlugs":["what-is-ai","machine-learning-basics"],"competencyKeys":["ai-concepts"],"practice":"Classify five everyday systems and explain which definition of AI you used."},{"title":"How systems learn","outcome":"Describe the role of examples, objectives, and generalization.","guideSlugs":["how-ai-learns","machine-learning-basics","ai-training"],"competencyKeys":["data-training"],"practice":"Diagnose why a model can perform well in testing but fail for a new population."},{"title":"Models and inference","outcome":"Explain what happens when a trained model produces an output.","guideSlugs":["ai-models","ai-inference","neural-networks"],"competencyKeys":["model-inference"],"practice":"Trace one user request from input through model output and human review."},{"title":"Evidence and uncertainty","outcome":"Check claims, benchmarks, and confident-sounding outputs.","guideSlugs":["ai-benchmarks","ai-hallucinations","ai-myths"],"competencyKeys":["evaluation","uncertainty","source-verification"],"practice":"Create a claim ledger for one AI product announcement."}],"capstone":{"title":"AI claim fact check","brief":"Choose one public AI claim and produce a one-page evidence review for a nontechnical reader.","evidence":["Original claim and source","Two supporting or contradicting sources","Known limitations","Plain-language conclusion with confidence"]}},{"slug":"responsible-ai-user","title":"Responsible AI User","audience":"Students, workers, creators, and everyday AI users","description":"Use AI productively while protecting privacy, checking outputs, and preserving human accountability.","level":"Beginner","estimatedHours":5,"prerequisites":["ai-literacy-foundations"],"objectives":["Design clear prompts with useful context and constraints.","Review outputs for factual, privacy, bias, and copyright risks.","Build a repeatable human-in-the-loop workflow."],"modules":[{"title":"Prompting with context","outcome":"Turn vague requests into testable instructions.","guideSlugs":["prompt-engineering","chatgpt-llms"],"competencyKeys":["prompt-design","llm-mechanics"],"practice":"Rewrite a weak prompt with audience, context, constraints, examples, and success criteria."},{"title":"Reviewing outputs","outcome":"Detect unsupported claims and decide what requires verification.","guideSlugs":["ai-hallucinations","ai-bias"],"competencyKeys":["output-review","bias-fairness","source-verification"],"practice":"Annotate an AI answer as verified, attributed, uncertain, or unsupported."},{"title":"Privacy and ownership","outcome":"Choose safe inputs and understand content rights.","guideSlugs":["ai-privacy","ai-copyright"],"competencyKeys":["privacy","copyright"],"practice":"Create a red/yellow/green data policy for an AI workflow."},{"title":"A dependable workflow","outcome":"Combine AI assistance with checkpoints and human ownership.","guideSlugs":["ai-daily-life","ai-writing"],"competencyKeys":["workflow-design","tool-selection","automation-safety"],"practice":"Map an existing task and mark where AI may draft, check, or never decide."}],"capstone":{"title":"Human-in-the-loop workflow","brief":"Design and test an AI-assisted workflow for a real recurring task.","evidence":["Before-and-after process","Prompt or configuration","Verification checklist","Privacy and failure safeguards"]}},{"slug":"ai-at-work","title":"AI at Work","audience":"Professionals, managers, job seekers, and small organizations","description":"Evaluate workplace use cases, run safe pilots, measure value, and communicate changes responsibly.","level":"Intermediate","estimatedHours":6,"prerequisites":["responsible-ai-user"],"objectives":["Select use cases using value, feasibility, and risk.","Run a bounded pilot with measurable success criteria.","Plan adoption without hiding workforce or governance impacts."],"modules":[{"title":"Use-case selection","outcome":"Prioritize tasks where AI can create measurable value.","guideSlugs":["ai-workflow-automation","ai-coding-tools"],"competencyKeys":["tool-selection","workflow-design"],"practice":"Score three candidate use cases on value, feasibility, reversibility, and risk."},{"title":"Pilots and experiments","outcome":"Design a comparison that can reveal whether AI actually helps.","guideSlugs":["ai-benchmarks","ai-workflow-automation"],"competencyKeys":["experiment-design","metrics","model-cost"],"practice":"Write a pilot plan with baseline, treatment, metrics, stop conditions, and review owner."},{"title":"People and jobs","outcome":"Assess task changes without reducing workers to job titles.","guideSlugs":["ai-jobs","ai-future"],"competencyKeys":["workforce","career-readiness","stakeholder-communication"],"practice":"Create a task-level impact map and reskilling plan."},{"title":"Operational governance","outcome":"Assign ownership for approvals, monitoring, and incidents.","guideSlugs":["ai-governance","ai-safety","ai-privacy"],"competencyKeys":["governance","incident-response","privacy"],"practice":"Build a lightweight responsibility matrix for one deployed workflow."}],"capstone":{"title":"AI pilot proposal","brief":"Produce a decision-ready pilot proposal for a real organization.","evidence":["Use-case scorecard","Baseline and success metrics","Risk controls","Adoption and review plan"]}},{"slug":"ai-policy-and-society","title":"AI Policy & Society","audience":"Educators, nonprofit leaders, journalists, advocates, and public servants","description":"Analyze AI systems through rights, equity, governance, safety, and public-interest outcomes.","level":"Intermediate","estimatedHours":6,"prerequisites":["ai-literacy-foundations"],"objectives":["Identify affected stakeholders and unequal impacts.","Compare governance tools and their enforcement limits.","Write evidence-grounded public-interest analysis."],"modules":[{"title":"Ethics and impact","outcome":"Distinguish values, harms, tradeoffs, and measurable outcomes.","guideSlugs":["ai-ethics","ai-bias"],"competencyKeys":["bias-fairness","public-interest"],"practice":"Create a stakeholder and impact map for one AI deployment."},{"title":"Privacy, security, and safety","outcome":"Separate privacy, misuse, accident, and security risks.","guideSlugs":["ai-privacy","ai-security","ai-safety"],"competencyKeys":["privacy","security","automation-safety"],"practice":"Build a threat-and-harm register with owners and mitigations."},{"title":"Law and governance","outcome":"Compare regulation, standards, audits, procurement, and internal controls.","guideSlugs":["ai-regulation","ai-governance"],"competencyKeys":["policy","governance"],"practice":"Compare two governance approaches against the same public problem."},{"title":"Public communication","outcome":"Explain evidence and uncertainty without hype or false balance.","guideSlugs":["ai-myths","ai-future"],"competencyKeys":["stakeholder-communication","source-verification","uncertainty"],"practice":"Write a 300-word public briefing with sources, limits, and a decision point."}],"capstone":{"title":"Public-interest AI briefing","brief":"Publish a sourced briefing on one current AI policy or social-impact question.","evidence":["Stakeholder map","Primary evidence","Policy options","Uncertainty and recommendation"]}},{"slug":"building-with-ai-systems","title":"Building with AI Systems","audience":"Technical learners and builders who need systems-level judgment","description":"Understand language models, retrieval, agents, evaluation, cost, and deployment safeguards through practical system design.","level":"Intermediate","estimatedHours":8,"prerequisites":["ai-literacy-foundations","responsible-ai-user"],"objectives":["Choose an architecture based on task and evidence needs.","Evaluate quality, latency, cost, and safety together.","Design monitoring and incident response before launch."],"modules":[{"title":"Language-model systems","outcome":"Reason about tokens, context, generation, and model tradeoffs.","guideSlugs":["chatgpt-llms","ai-models","ai-inference"],"competencyKeys":["llm-mechanics","model-inference","model-cost"],"practice":"Compare two model options using quality, latency, context, privacy, and cost."},{"title":"Grounding with retrieval","outcome":"Know when and how retrieval can improve evidence access.","guideSlugs":["retrieval-quality","vector-databases","embeddings"],"competencyKeys":["rag","source-verification"],"practice":"Design a retrieval test set with answer and citation requirements."},{"title":"Agents and tools","outcome":"Bound multi-step systems with permissions and checkpoints.","guideSlugs":["ai-agents","ai-workflow-automation"],"competencyKeys":["agents","automation-safety","security"],"practice":"Write an agent permission model and failure-recovery path."},{"title":"Evaluation and operations","outcome":"Measure system performance before and after deployment.","guideSlugs":["ai-benchmarks","ai-safety"],"competencyKeys":["evaluation","metrics","incident-response","experiment-design"],"practice":"Create an evaluation suite covering quality, refusal, latency, cost, and regressions."}],"capstone":{"title":"AI system design review","brief":"Produce an architecture and evaluation plan for a source-grounded AI application.","evidence":["Architecture diagram","Evaluation dataset","Cost and latency budget","Security, monitoring, and rollback plan"]}}]}