Tips, guides, and insights on AI-powered marking, assessment strategies, and education technology.
Tips and practical guides on AI marking and assessment strategies.
The A-Level Art personal study is a substantial written essay produced at home over weeks — exactly the kind of extended writing AI imitates well. A practical guide for art teachers on checking the written investigation, reading likelihood scores, and keeping it fair.
A-Level Accounting mixes numerical accounts with extended written analysis and evaluation. A guide for accounting teachers on using GradeOrbit to mark the written responses faster against your mark scheme, and where teacher judgment on own-figure numerical work stays essential.
Building next year’s assessment and data-capture calendar over the summer? A guide for SLT on where AI marking fits between assessment points, protecting turnaround, and sizing a shared credit pool to the calendar you set.
The A-Level Computer Science NEA carries a large written report — analysis, design, testing and evaluation — that is easy to generate with AI. This guide shows how UK teachers can check the prose fairly using likelihood scores and professional judgment.
Core Maths papers are marked on method — modelling, estimation, working with real data and calculation steps — not just the final answer. This guide shows how UK teachers can mark method marks and working-out faster with AI support.
When classes move to new teachers over the summer, marking consistency and shared standards are the first things to slip. This guide shows how school leaders can use AI marking software to make handover smoother across a department.
Philosophy essays are one of the easiest places for AI to hide, because abstract argument is exactly what a chatbot does fluently. This guide helps A-Level Philosophy teachers read essays critically and use likelihood scores fairly.
BTEC Sport marking means reading long assignments against detailed Pass, Merit and Distinction criteria, over and over. Learn how AI marking helps you apply the assessment criteria consistently without losing rigour.
Key Stage 3 generates a huge volume of low-stakes marking across every subject and class, with little of the exam-board scaffolding that supports GCSE. This guide looks at how AI marking software can protect teacher time and sharpen consistency for heads of KS3.
Book reviews and reading logs are quick for a student to generate with AI. Learn how to use likelihood scores to check reading tasks fairly while keeping your professional judgment in charge.
A-Level PE papers mix short recall with extended application and evaluation answers across anatomy, biomechanics and sport psychology. Learn how AI marking helps you apply the mark scheme consistently without losing rigour.
A school's teaching and learning policy sets the expectations for marking, feedback and assessment. Here is how AI marking software fits inside that policy without overreaching what it can honestly claim.
Creative writing is one of the hardest places to detect AI-generated work, because good imaginative prose is meant to sound polished. This guide helps UK English teachers read GCSE creative writing critically and use likelihood scores fairly.
GCSE physics calculation questions are marked on method, not just the final answer — which makes them slow to mark by hand. This guide shows how UK science teachers can mark working-out, method marks, and units faster with AI support.
History departments carry a heavy essay-marking load — source analysis, interpretations, and extended answers across every year group. This guide looks at how AI marking software can protect teacher time and sharpen consistency for heads of history.
Year 10 and sixth-form work experience reports are easy to draft with AI. Learn how to use likelihood scores to check reflective write-ups fairly while keeping your professional judgment in charge.
A-Level Further Maths papers are dense with method marks and multi-step working. Learn how AI marking helps you award method marks consistently across handwritten scripts without losing rigour.
When SLT write the school self-evaluation form, teacher workload and assessment quality are recurring themes. Here is how AI marking software gives you evidence for both in your SEF and improvement plan.
Sixth-form tutors are seeing more AI-drafted UCAS personal statements. Learn how to use likelihood scores to check drafts fairly while keeping your professional judgment in charge.
A-Level Maths marking means tracking method marks through pages of working. See how GradeOrbit supports Edexcel, AQA and OCR mark schemes to help you assess working-out consistently and faster.
Heads of science carry the heaviest marking load in the school — three sciences, required practicals and mocks. See how AI marking software cuts that load while protecting standards.
GCSE Combined Science practical write-ups are increasingly written with AI help. Learn how teachers use likelihood scores to check method, analysis and conclusion sections fairly.
GCSE Latin translation and comprehension papers are painstaking to mark. See how GradeOrbit supports OCR and Eduqas Latin mark schemes to help classics teachers work through scripts faster.
Assistant headteachers own workload, teaching and learning, and assessment quality. See how AI marking software helps you cut marking load across teams while protecting standards.
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