Amo.ng topic hub
Use AI responsibly across teaching, learning, assessment, curriculum, student activities and educational administration with clear evidence and human oversight.
Effective educational AI should strengthen learning rather than replace learner judgment, teaching responsibility or institutional accountability. This hub brings together Amo.ng resources for coursework, assessment design, academic integrity, curriculum alignment, student AI projects, workshops, accessibility and responsible educational technology decisions.
Use the collection to design learning activities, document appropriate AI use, test assessment fairness, prepare student teams, evaluate educational tools and connect institutional AI policy with practical teaching and learning.
Document AI inputs, accepted and rejected outputs, source checks, student revisions and learner-supplied learning evidence without certifying authorship or institutional compliance.
Test an assessment brief for unclear requirements, construct-irrelevant difficulty, accessibility barriers, ambiguous AI-use rules and inconsistent marking risk before release.
Trace programme outcomes across modules, teaching activities and assessments to expose underassessment, duplication, progression breaks and evidence gaps.
Turn a defined AI topic into a practical club workshop with source-grounded explanations, exercises, facilitator cautions and observable before-and-after learning evidence.
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.
Move from a bounded academic question through source triage, a pre-draft evidence ledger, learner-owned argument development, claim verification, and an assessor-owned oral defence.
Run a permissioned campus AI hackathon with bounded challenges, accessible facilitation, safe data and tools, controlled prototypes, accountable judging, and post-event learning evidence.
Move from approved university AI policy through procurement, curriculum alignment, implementation oversight, and evidence-based institutional impact decisions with accountable authority at every gate.
Audit documents, slides, video, audio, images, assessments, and LMS delivery for accessibility barriers, then prioritize learning-equivalent remediation and verification.
Maintain a proportionate, honest record of material AI inputs, decisions, source checks, revisions and learning reflection without declaring authorship or policy compliance.
Trace a learner’s quantitative work to the earliest unsupported step, test competing error explanations, and design a fresh transfer task that measures reasoning rather than answer imitation.
Evaluate an educational AI acquisition against learning need, vendor evidence, accessibility, student-data controls, security, equity, total cost, lock-in, and accountable approval requirements.
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