AI Marking Software and School Feedback Policy Reviews
The end of the summer term is when many schools sit down to review their marking and feedback policy for the year ahead. It is the natural moment: data drops are in, exam classes have gone, and senior leaders have a brief window to look honestly at what the current policy is actually costing staff and whether it is buying the impact it promises. Increasingly, that review now has to grapple with a question that did not exist a few years ago — where, if anywhere, does AI marking software fit into the school's feedback policy? This guide is written for the deputy heads, assistant heads, and teaching-and-learning leads holding the pen on that policy, and it frames AI marking as what it is: a workload tool, not a replacement for professional judgement.
Why Feedback Policies Get Reviewed in Summer
A marking and feedback policy is one of the highest-stakes documents a school writes, because it directly governs how teachers spend their evenings and weekends. The Education Endowment Foundation has long made the point that more marking is not the same as better feedback, and the Department for Education's workload reduction work has repeatedly identified excessive written marking as a leading driver of teacher workload and a factor in retention. A summer review is the chance to act on that — to strip out marking that generates effort without learning, and to be clear about what genuinely moves students forward.
The trouble is that "do less marking" is easy to write and hard to deliver, because the underlying demand has not changed. Students still need timely, specific feedback; books still need to show that learning is being responded to; and leaders still need assurance that standards are consistent across departments. A policy that simply reduces the frequency of marking without changing how it is produced tends to drift back to old habits within a term. This is precisely the gap that AI marking can help close, if the policy frames it carefully.
What AI Marking Can Realistically Contribute
AI marking software does not mark for the teacher in the sense of replacing their judgement. What it does is take the first heavy pass — transcribing student work, assessing it against criteria the teacher has defined, proposing a grade and categorised feedback — so the teacher reviews and refines rather than starting from a blank page. For a feedback policy, that distinction is everything. The professional decision stays with the teacher; the mechanical labour that makes marking unsustainable is what gets reduced.
GradeOrbit lets a teacher set up marking criteria once — qualification level, exam board, the specific task and mark scheme — and then marks each student's work against that rubric consistently, returning a marks-based grade and feedback mapped to the criteria. Because the standard is fixed before marking begins, AI support can actually improve the consistency a feedback policy is meant to guarantee, which is often where moderation across a department breaks down. Our piece on where teacher marking time actually goes sets out which parts of the job are mechanical bulk and which require the teacher — a useful map when you are deciding what a policy should and should not mandate.
Writing AI Into the Policy Without Lowering Standards
The risk a leader has to manage is the perception — internal or from inspection — that AI marking means corners are being cut. The way to manage it is to be explicit in the policy text. State that AI marking is used as a first-pass tool, that a teacher reviews and is accountable for every grade and every piece of feedback that reaches a student, and that the professional judgement of the teacher overrides the tool in every case. A policy framed that way is fully consistent with Ofsted's stated position that it does not expect any particular frequency or form of marking, only that feedback is effective; our guide to Ofsted marking expectations covers that ground for leaders building the rationale into a policy.
It also helps to be specific about where AI marking is and is not appropriate in your context. A policy might encourage it for routine formative tasks and mock exam scripts, where the time saving is largest, while reserving certain high-stakes or formally moderated pieces for the existing process. Naming those boundaries in the policy prevents the inconsistent, ad-hoc adoption that undermines confidence, and gives heads of department a clear framework to work within.
Handwritten Work and Data Protection
Two practical questions always come up in a policy review, and a leader should have answers ready. The first is handwriting: most marking in secondary schools is still of handwritten work, and any tool the policy endorses has to cope with it. GradeOrbit reads handwritten scripts — teachers photograph or scan pages, including via a phone camera over a QR code for whole class sets — so the policy does not have to carve out an exception for the majority of marking.
The second is data protection, which any senior leader is right to scrutinise. In the individual teacher workflow, student work is redacted of identifying details before processing — the redaction is burnt into the image in the browser, so nothing identifying leaves the device — and the work is never stored; it is processed and discarded once the grade and feedback are returned. A policy can state that plainly. Where a school wants to adopt AI marking at institutional scale, with student work retained for staff to return to, that is handled under a separate, data-processing-agreement-backed school arrangement rather than the never-store individual model, and a review is the right moment to decide which route fits your school.
Piloting Before You Commit the Policy
A sensible feedback policy review does not bet the school on an untested tool. The stronger move is to write a short pilot into the plan — one or two departments, one term — and let the policy formally adopt AI marking only once you have evidence of the time saving and the feedback quality in your own context. Our guide to piloting AI marking software in your school sets out how to structure that, and the companion piece on reducing teacher workload across your school places it within the wider workload picture leaders are trying to shift.
Approached this way, AI marking becomes a deliberate, evidenced part of a feedback policy that does less of the work that does not help and protects the time teachers spend on the work that does. The standards the policy guarantees stay intact, because the teacher remains accountable for every judgement; what changes is how much of their life the policy quietly demands.
Explore GradeOrbit for Your School
If your summer feedback policy review is weighing how AI marking could cut workload without lowering standards, GradeOrbit is built to sit inside exactly that kind of policy — teacher-led, criteria-driven, and designed to assist rather than replace. New accounts get a small allocation of free credits so a department can try it on real student work as part of a pilot before any commitment.
Visit gradeorbit.co.uk to learn more and start a conversation about what AI marking could look like in your school.