AI marking
AI marking is the use of artificial intelligence to assess student work against defined criteria - a mark scheme or a teacher's own marking guidelines - and to draft marks and feedback for a teacher to review.
What is AI marking?
AI marking is the use of artificial intelligence to assess student work and draft marks and feedback. Rather than replacing the teacher, well-designed AI marking produces a first pass against a mark scheme or the teacher's own guidelines, which the teacher then reviews, adjusts and approves before anything reaches a student.
The phrase covers a wide spectrum of tools, from a generic chatbot prompted to grade an essay to purpose-built platforms that apply an exam board's mark scheme criterion by criterion. The difference matters. A criterion-referenced system produces marks a teacher can audit - each point tied to evidence in the answer and to the criterion it meets - while a general-purpose model produces an overall impression that is hard to interrogate and can drift from one script to the next.
How AI marking works
In a typical workflow, the teacher sets the task and supplies the criteria: the mark scheme, a set of marking guidelines, or both. The system marks each submission against those criteria and drafts feedback for the student. The teacher then reviews the draft marks, adjusts anything they disagree with, and approves the work before results or feedback are released. In British-curriculum secondary schools the criteria are usually GCSE or A-Level mark schemes and their assessment objectives, which is why alignment to the specific board's scheme - rather than a generic notion of quality - is what teachers look for first.
What AI marking is not
AI marking is not automated grading. A well-designed system does not decide final marks; it drafts them, and the teacher remains the decision-maker throughout. Nor is it a substitute for professional judgement - the teacher's standard is the standard, and the tool's job is to apply it consistently across a class set, not to invent its own.
Key takeaways
- AI marking uses artificial intelligence to draft marks and feedback against defined criteria, such as an exam board's mark scheme.
- Criterion-referenced AI marking is auditable: each mark is tied to specific evidence in the student's answer.
- In a teacher-controlled workflow, the AI produces a first pass and a teacher reviews and approves every mark before it reaches a student.
- AI marking is distinct from generic chatbots, which mark by general impression rather than to your criteria.
Frequently asked questions
Is AI marking the same as using ChatGPT to mark?
No. A generic chatbot marks by overall impression and can drift from one script to the next. Purpose-built AI marking applies a defined mark scheme criterion by criterion, keeps marking consistent across a class set, and fits a review-and-approve workflow in which the teacher stays the final decision-maker.
Will AI marking replace teachers?
No. Marking.ai drafts; teachers decide. Nothing is released until a teacher approves it. It removes the repetitive first pass so teachers spend more time teaching and on the judgement only they can make.
Related terms
Mark scheme
A mark scheme is the document that sets out how marks are awarded for each question in an assessment - the answers or qualities that earn credit, and how many marks each is worth.
Moderation
Moderation is the process by which teachers compare samples of marked work to check that a mark scheme has been applied consistently, so the same work would receive the same mark whoever marked it.
Criterion-referenced assessment
Criterion-referenced assessment judges work against defined standards of what a student should know or be able to do, rather than against how other students performed.
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