For institutions navigating AI pressure on assessment without a clear sense of which next step is theirs.
Outcome: a clearer route into the three main ways into the work, with the assessment design page for departments where the task itself needs attention first.
Written for senior leaders, digital education and quality teams, academic development colleagues, and departments asking how their current assessment, feedback and platform processes should adapt in the AI era.
What AI changes, and what it exposes
AI is putting pressure on assessment design, academic integrity, feedback practice, marking workflows, student guidance, staff confidence and institutional decision-making.
It also lands inside existing assessment processes, Moodle and platform workflows, quality expectations, marking and moderation practices, and local departmental workarounds.
A practical response needs to connect policy, pedagogy, workflow, platform decisions and student and staff adoption, rather than treating AI as a separate issue sitting outside the assessment process.
The aim is not to remove all effort from assessment. Some effort needs to stay: interpreting criteria, giving meaningful feedback, moderating fairly, supporting students and exercising academic judgement. The focus is reducing avoidable workload while protecting the human judgement required for effective assessment.
How I help
Work in this area moves from abstract AI strategy into practical implementation.
Support can include reviewing assessment and feedback practice, identifying where workflows or guidance are breaking down, clarifying AI-era risks, supporting platform and process decisions, and helping institutions design workable next steps.
My goal is to help teams respond with more confidence and consistency: protecting standards, supporting staff, and making assessment practice more workable in the AI era.
Where this work often leads
AI-era assessment work usually leads in one of three directions.
Sometimes the assessment task itself needs attention: what it is trying to evidence, how students can show judgement, whether process evidence is needed, and how AI use should be framed.
Sometimes the workflow needs redesign: marking, moderation, feedback, Moodle and platform use, grade handling, exceptions and staff guidance.
Sometimes a pilot is needed: a structured test of a tool, workflow or assessment approach before wider rollout.
The value is in knowing which kind of work is actually needed before committing time, money or staff energy.
Where to start
Most AI-related assessment work begins in one of four ways:
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Assessment & AI Workflow Diagnostic, when you need a clear view of current risks, friction and priorities.
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Assessment Design for the AI Era, when the assessment task itself needs attention before workflow or platform decisions.
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Assessment Workflow Redesign Sprint, when marking, moderation, feedback or platform workflows need redesign.
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Fractional Assessment Transformation Partner, when your institution needs ongoing support across a live programme of AI-era assessment change.
Related work
See also:
Book a scoping conversation
If your institution is navigating AI-era assessment pressure and wants help finding the most useful starting point, I’d be glad to hear more.