AI Club Workshop Facilitator and Learning-Evidence Pack
Turn a defined AI topic into a practical club workshop with source-grounded explanations, exercises, facilitator cautions and observable before-and-after learning evidence.
Searchable AI prompt library
Structured instructions for completing individual tasks with an AI tool. Filter by outcome, category, tool, difficulty or keyword.
Open a Prompt to copy its instructions, run supported Prompts privately in Workplace, or prepare a task for Codex where available. Copying text does not run the Prompt.
Turn a defined AI topic into a practical club workshop with source-grounded explanations, exercises, facilitator cautions and observable before-and-after learning evidence.
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.
Trace programme outcomes across modules, teaching activities and assessments to expose underassessment, duplication, progression breaks and evidence gaps.
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.
Investigate scorer patterns, criterion distributions, anchor decisions and justified exceptions for possible marking drift without automatically changing grades.
Test an assessment brief for unclear requirements, construct-irrelevant difficulty, accessibility barriers, ambiguous AI-use rules and inconsistent marking risk before release.
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.
Document AI inputs, accepted and rejected outputs, source checks, student revisions and learner-supplied learning evidence without certifying authorship or institutional compliance.
Convert assigned sources into a claim-to-evidence ledger that separates source findings, student interpretation, counterevidence and unresolved uncertainty before drafting.
Inspect a research package for data provenance, licensing, environment, code, seeds, dependencies and expected outputs, separating reproduced evidence from inaccessible work.
Build and stress-test a qualitative codebook using traceable excerpts, rival interpretations, coder disagreement, positionality and documented adjudication.
Cross-check hypotheses, outcomes, exclusions, power assumptions, models, multiplicity controls and decision rules before data inspection, with deviations made explicit.