Assessment should make the intended evidence of learning clearer. That can include process evidence, oral explanation, staged drafts, authentic application, reflection and explicit rules about when AI is allowed. The goal is not to make every task “AI-proof”. It is to design assessment that still gives useful evidence of what the learner understands and can do.
You will examine how AI changes common assessment assumptions, identify vulnerable task types, design stronger evidence of learning and communicate permitted and prohibited AI use more clearly.
What the course covers
The modules focus on practical educator workflows rather than technology for its own sake.
Risks, opportunities and new assumptions.
Authenticity, process evidence and assessment redesign.
Clear rules, disclosure, attribution and proportionate responses.
Consistent expectations, documentation and review.
Designed for practical education work
Activities include revising an existing assignment, creating an AI-use statement, planning evidence checkpoints and responding proportionately when AI misuse is suspected.
Responsible use matters
AI output should be treated as draft material that requires professional judgement, verification and adaptation. Educators remain responsible for accuracy, safeguarding, privacy, assessment decisions, intellectual-property considerations and the suitability of materials for their learners and setting.
Build the AI skills that fit your role
Compare AI literacy, prompt engineering, assessment, lesson planning and tool-specific pathways before choosing a course.