Category
Prompts for lesson planning, tutoring, curriculum design, rubric creation, assessment, and diagnosing student misconceptions.
Help keep Amo.ng free and public
Convert assigned sources into a claim-to-evidence ledger that separates source findings, student interpretation, counterevidence and unresolved uncertainty before drafting.
Document AI inputs, accepted and rejected outputs, source checks, student revisions and learner-supplied learning evidence without certifying authorship or institutional compliance.
Locate the earliest unsupported step in a worked quantitative solution, compare plausible error explanations and create a changed problem that gathers item-level evidence of reasoning transfer.
Test an assessment brief for unclear requirements, construct-irrelevant difficulty, accessibility barriers, ambiguous AI-use rules and inconsistent marking risk before release.
Investigate scorer patterns, criterion distributions, anchor decisions and justified exceptions for possible marking drift without automatically changing grades.
Create adaptive oral questions tied to a submitted artefact, with follow-ups that test reasoning, sources, decisions and limitations without treating fluency as proof of authorship.
Trace programme outcomes across modules, teaching activities and assessments to expose underassessment, duplication, progression breaks and evidence gaps.
Design a reproducible learning lab that hides model identity, uses held-out tasks and calibrated scoring, and teaches students to interpret uncertainty and failure slices.
Turn a defined AI topic into a practical club workshop with source-grounded explanations, exercises, facilitator cautions and observable before-and-after learning evidence.
Test whether a student AI prototype has documented users, data rights, evaluation evidence, limitations, risks and a safe accountable owner before demonstration or reuse.
Translate supplied university AI policy into traceable role-specific rules, evidence requirements, escalation routes and fair appeals without inventing institutional authority.
Evaluate an educational AI purchase against learning need, evidence, accessibility, privacy, security, equity, retention, lock-in and total cost before an accountable decision.