How to Use AI Detection for GCSE Coursework
AI-generated text is now commonplace in secondary schools. Tools like ChatGPT and Claude are free, fast, and capable of producing coursework-quality writing in seconds. For teachers responsible for assessing GCSE coursework components — whether that is controlled assessment, non-examined assessment, or extended written tasks — the question is no longer whether students might use AI, but how to identify it fairly when they do.
This guide explains how AI detection for GCSE coursework works in practice, what likelihood scores actually tell you, and how to use GradeOrbit's built-in detection tool to support your professional judgment rather than replace it.
How AI Detection Works: Probability, Not Proof
AI detection tools do not read student work the way a teacher does. They analyse statistical patterns in the text — the predictability of word sequences, the regularity of sentence structure, the distribution of vocabulary, and the uniformity of tone. These patterns are then compared against a model trained on large volumes of both human-written and AI-generated text. The output is a likelihood score: a percentage that represents how closely the text resembles AI-generated content.
The critical thing to understand is that a likelihood score is probabilistic. A score of 85% means the text shares strong statistical characteristics with AI-generated writing — it does not mean a student definitely used a tool like ChatGPT or Claude. False positives occur. Students who write precisely, who follow teacher modelling closely, or who have a naturally concise and well-structured style can produce work that scores higher than you might expect.
This is why detection results must always be treated as the beginning of an inquiry, not a verdict. The score is a prompt to look more carefully — not a finding in itself.
What a High Likelihood Score Actually Tells You
When a piece of GCSE coursework returns a high likelihood score, the most useful thing you can do is read the work again alongside everything else you know about that student. Consider whether the vocabulary and sentence construction is consistent with their in-class writing. Check whether they produced drafts. Think about how they engage with written tasks in lessons and whether this submission reflects their usual voice.
AI-generated text tends to have particular characteristics that teachers often recognise on re-reading with fresh eyes: an unusual evenness of tone, transitions that are structurally correct but feel slightly generic, a tendency to address the question at a high level without the specific examples or personal missteps that characterise genuine student work. ChatGPT and Claude both produce text with these qualities, though they differ in style — Claude tends toward longer, more discursive sentences, while ChatGPT often produces more listicle-style structure even in prose mode.
A high score on a submission from a student who consistently performs at that level, engaged in class discussion, and submitted drafts should be treated very differently from a high score on a late submission from a student who rarely produces extended written work. The score tells you something about the text; your professional knowledge tells you the rest.
For detailed guidance on what to do once you have a result, the post on how to handle AI detection scores responsibly walks through the decision-making process step by step.
GradeOrbit's Built-In AI Detection Tool
GradeOrbit includes a dedicated AI Detection workflow that operates independently from the marking tools. You can upload a scanned image of handwritten work, paste text directly, or upload a typed document. The tool processes the submission and returns a likelihood score from 0 to 100%, along with the specific linguistic signals that contributed to the result.
There are two model options, and the right choice depends on your purpose.
The Faster model costs 1 credit per submission. It is designed for a quick initial sweep — useful when you want to check a class set before deciding whether any pieces warrant closer scrutiny. It returns a score and a confidence label, giving you a clear overview without going into deep detail on every submission.
The Smarter model costs 3 credits per submission. It runs a more thorough analysis and produces a detailed breakdown of the signals identified in the text. This level of detail is appropriate when you are considering raising a formal concern, involving a head of year or exams officer, or preparing documentation for a malpractice investigation. The additional detail makes it much easier to explain your reasoning to colleagues and, if necessary, to the student themselves.
For routine checks on a full GCSE cohort, the 1-credit model gives you a useful first pass. For any submission where you intend to take action, the 3-credit model provides the evidence base you need.
How to Act on Results Fairly
A high likelihood score on its own is never sufficient grounds for a formal action. The appropriate first step is always a professional conversation. Ask the student to walk you through their work — where their ideas came from, how they structured their response, what they found difficult. A genuine author will be able to discuss their work with some fluency, even if they struggle to reproduce it exactly. A student who used AI extensively will often find it much harder to explain specific choices or to recall the reasoning behind particular sections.
If the conversation raises further concern, ask the student to complete a short supervised writing task on the same topic. A significant disparity between the supervised work and the submitted coursework is meaningful evidence. Document your reasoning at every stage: the score, the signals, your professional observations, the outcome of any conversation.
Your school's academic integrity policy and the JCQ guidance on AI use in assessments set out the formal procedures from this point. Exam boards including AQA, Edexcel, and OCR all have defined malpractice procedures that your exams officer will be familiar with. The key principle throughout is that the student must be given the opportunity to respond before any formal consequence is applied.
Building a Consistent Departmental Approach
One of the most common mistakes schools make with AI detection is allowing individual teachers to develop their own thresholds and processes. If one member of a department runs detection checks on every submission and another does not, students are not being treated equitably — and the department has no defensible basis for action if a case is challenged.
Agree as a department on when checks will be run, what score level triggers further investigation, and how results will be recorded. The guidance on using AI detection in school fairly sets out a practical framework for building a consistent, auditable process. Embed the approach into your existing assessment policy rather than treating it as a separate procedure.
It is also worth communicating clearly with students before coursework season begins. A plain-English explanation of what constitutes AI misuse, delivered in class and reinforced in writing, reduces the likelihood of students stumbling into a policy breach and makes any subsequent process much more straightforward.
Try GradeOrbit's AI Detection Tool
AI detection is now a core skill for any teacher responsible for assessing GCSE coursework. Used properly — as a tool to support professional judgment rather than replace it — it helps you protect the integrity of your students' qualifications and identify where intervention is genuinely needed.
GradeOrbit's AI Detection tool is built for teachers, not for IT specialists. Upload a submission, choose your model, and receive a clear, documented result you can act on responsibly. Try GradeOrbit free and run your first detection check today.