AI Literacy: Make, Check and Explain an AI-Assisted Answer
Build practical AI literacy by distinguishing generation from verification, protecting information and documenting an informed human decision.
Read the guide →Learn · Apply · Verify
One useful task at a time. Each guide includes examples, a practice activity and a reusable prompt.
Build practical AI literacy by distinguishing generation from verification, protecting information and documenting an informed human decision.
Read the guide →Turn vague intentions into observable learning goals, align practice and assessment, and check whether a generated plan measures the right skill.
Read the guide →Create a realistic study schedule using task evidence, spaced practice, retrieval and a clear rule for adjusting the next session.
Read the guide →Design a bounded tutoring conversation with diagnostic questions, graduated hints, independent answers and a clear stopping rule.
Read the guide →Map an analogy to a difficult concept, explain where the comparison fails and assess understanding without relying on the analogy.
Read the guide →Create useful AI-assisted practice questions with a worked percentage example, checked answers, a reusable prompt and a 15-minute review activity.
Read the guide →Use criteria and quoted evidence to draft feedback, verify it against a fictional response and design a next step the learner can act on.
Read the guide →Plan a 60-minute lesson with a reusable AI prompt, a worked teaching example and a practical review checklist.
Read the guide →Adapt a learning resource without losing its meaning. Use a worked instruction rewrite, a text alternative and a practical accessibility review.
Read the guide →Turn vague marking language into evidence-based criteria. Test an illustrative rubric against two responses before using it for assessment.
Read the guide →Set task-specific AI boundaries, teach useful declarations and respond to concerns through evidence and a fair institutional process.
Read the guide →Use a repeatable workflow to check AI claims, calculations and references before using them in teaching or professional work.
Read the guide →Replace promises of “AI-proof” tasks with aligned evidence of learning, process checkpoints and proportionate follow-up questions.
Read the guide →Design graduated support for a shared learning outcome, with a worked example, equivalent practice routes and checks that preserve learner independence.
Read the guide →Plan a visual around one learning relationship, distinguish illustration from evidence, and verify labels, data and accessible alternatives.
Read the guide →Turn a broad interest into a searchable question, keep a search log and evaluate evidence rather than treating generated references as research.
Read the guide →Use a review matrix to compare studies, preserve disagreement and draft a synthesis that can be traced back to the original sources.
Read the guide →Keep an accurate record of AI assistance, distinguish disclosure from source citation and apply the current style required for your work.
Read the guide →Turn broad principles into usable rules for data, assessment, procurement and responsibility, with an approval-ready example and scenario checks.
Read the guide →Map outcomes, AI-supported activities and independent evidence into a coherent course sequence, then test one unit before scaling.
Read the guide →Adapt six AI prompt templates for lesson planning, explanations, practice questions, feedback, rubrics and accessible materials.
Read the guide →Learn a practical approach to sensitive data, prompt injection, account security and human approval when using AI at work.
Read the guide →Build practical AI skills with a four-week plan covering prompting, verification, safe workflows and a small portfolio project.
Read the guide →Improve an AI draft with a clear review checklist, a worked packet-loss example and a reusable critique prompt.
Read the guide →New to AI? Start with the four-week learning plan. Already working on a task? Use the free resources.