Tips, guides, and insights on AI-powered marking, assessment strategies, and education technology.
Tips and practical guides on AI marking and assessment strategies.
Heads of RE: see how GradeOrbit helps your department mark extended evaluative writing faster, keep standards consistent across teachers and faiths, and free up time, under one school subscription.
Take-home assessments give students time, privacy, and unsupervised access to AI tools — the exact conditions where generated work is hardest to spot. A practical guide for teachers on checking unsupervised work, reading likelihood scores, and keeping the process fair.
GCSE Statistics papers mix calculation, interpretation, and extended written reasoning — a marking load that eats evenings. Here is how AI can speed up the routine work while you keep the judgment.
A homework policy is only as good as the feedback behind it — and feedback is where most policies quietly break down under teacher workload. Here is how AI marking software can help schools make their homework policy deliverable.
Baseline tests land in week one and every set is due back at once. See how GradeOrbit marks scanned, handwritten baseline assessments against your own rubric in minutes.
A practical framework for SLT writing a school AI marking policy: scope, data protection, the teacher-judgment line, and how to keep it auditable for governors and Ofsted.
Checking one or two suspicious essays is easy. Screening an entire class set fairly is harder. Here is how teachers run AI detection across a whole class without creating inconsistent outcomes.
A-Level Classical Civilisation essays demand close reading of sources, context, and argument — and that makes them slow to mark. Here is how AI can speed up the routine work while you keep the judgment.
Feedback is one of the highest-impact, lowest-cost levers in any pupil premium strategy. Here is how AI marking software can help schools deliver it more consistently to disadvantaged students.
Lab reports have a fixed structure that AI imitates well, which makes generated write-ups hard to spot. A practical guide for science teachers on checking practical write-ups, reading likelihood scores, and keeping the process fair.
A-Level computer science projects are long, technical, and slow to mark against a detailed assessment grid. A guide for teachers on using AI to speed up the written analysis and evaluation while keeping professional judgment in charge.
Book looks and work scrutiny are how schools check that feedback is consistent and effective. A guide for leaders on where AI marking software fits into work sampling, and how it strengthens rather than threatens the process.
Fabricated citations are one of the clearest tells of AI use in coursework. A practical guide for teachers on checking references, reading likelihood scores, and keeping the process fair.
T-Level assignments are long, criteria-heavy, and time-consuming to mark. A practical guide for teachers on using AI to speed up grading while keeping professional judgment in charge.
Workload is now a standing item in teacher appraisal. A guide for school leaders on where AI marking software fits into performance management, objectives, and wellbeing conversations.
Homework set through Google Classroom and Teams is the easiest work for a student to generate with AI. A practical guide for teachers on running a fair detection check on online submissions without treating a score as a verdict.
Health and Social Care coursework is long, criteria-heavy, and slow to mark by hand. Here is how AI marking speeds up the first pass across a class without compromising on rigour or your professional judgement.
Whole-school literacy depends on consistent written feedback across every subject — and that is exactly where workload and inconsistency bite. Here is how literacy coordinators can use AI marking software to support staff without lowering standards.
Year 12 personal statement drafts land over the summer, and they are a high AI-risk piece. A practical guide for teachers on reading likelihood scores fairly and supporting students honestly.
GCSE German writing tasks are slow to mark by hand — checking tense accuracy, word order, and content points across a whole class takes hours. Here is how AI marking speeds it up without losing rigour.
Summer is when many schools review their marking and feedback policy. Here is how senior leaders can factor AI marking software into a policy that cuts workload without lowering standards.
The A-Level Geography NEA is built on a student's own fieldwork, which makes AI-generated write-up sections stand out — if you know where to look. Here is how to detect AI in NEA reports fairly, read likelihood scores, and handle high results without accusing a student.
Year 13 mocks land at the worst possible time and carry the highest stakes — they feed predicted grades and UCAS references. Here is how to mark Year 13 mock exam papers faster with AI without cutting corners on the feedback students need.
An Ofsted subject deep dive looks for assessment that informs teaching and feedback that is consistent across a department — not for proof that books are heavily marked. Here is how AI marking software supports a deep dive for subject leaders and SLT.
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