Assessment, feedback and AI
Practical insights and resources for universities reviewing assessment, feedback and AI-era change.
This collection focuses on assessment design, evidence of learning, marking and moderation workflows, Moodle/platform processes, assessment tools, pilot planning, staff adoption and implementation.
The aim is to help university teams move from broad AI concern into practical decisions about what assessment should evidence, how feedback should support learning, which workflows need redesign, and where tools or platforms can genuinely help.
In Brief
AI is making older assessment pressures harder to ignore: evidence of learning, feedback quality, marking workload, Moodle/platform processes, staff confidence, student guidance and the gap between policy and practice.
The posts and resources in this collection explore how universities can respond with more care and structure: refining assessment design, making student judgement and process more visible, reducing avoidable workflow friction, choosing tools more carefully, and supporting staff adoption.
If you are not sure where to start, begin with the practical guide or the market map below.
Featured practical guide
A practical guide for universities reviewing assessment design, feedback practice, marking and moderation workflows, Moodle/platform processes, staff adoption and AI-era change.
Featured market map
A practical market map for universities reviewing assessment tools, AI-era workflows, Moodle and Canvas integration, marking, moderation, feedback, pilot design and staff adoption.
