AI Detection for Criminology Coursework: A Teacher's Guide

Criminology is one of the fastest-growing subjects in UK sixth forms and colleges, and its assessment structure makes it unusually exposed to AI-generated writing. The WJEC Level 3 Diploma is built around controlled assessments that ask learners to analyse crime data, evaluate campaigns, and write extended reasoned arguments about the criminal justice system. Those are precisely the tasks that ChatGPT and Claude handle fluently. If you deliver Criminology, the question is no longer whether learners might lean on these tools — it is how you assess fairly when they do.

This guide covers AI detection for Criminology coursework: how a probabilistic likelihood score actually works, what to do when a controlled assessment scores high, and how to combine detection results with your professional judgment as the assessor.

Why Criminology Coursework Is Especially Exposed

Unit 2 and Unit 4 of the WJEC Diploma are internally assessed under controlled conditions, but the preparation and drafting that surrounds them often happens away from your classroom. Learners research campaigns, gather evidence, and plan responses before the formal write-up. That preparation window is where AI tools get used — a learner might ask a chatbot to summarise the arguments for and against a particular sentencing policy, then reproduce that structure in their assessment.

The writing that results tends to be fluent, well-organised, and confident. It hits the assessment criteria for analysis and evaluation on the surface. But when you ask the learner to explain how they reached a conclusion, or to defend a claim about recidivism rates, the understanding is not there. That gap between polished writing and shallow comprehension is the classic signature of over-reliance on AI.

The Evaluation Problem

Criminology rewards evaluation — weighing theories, judging the effectiveness of policies, reaching justified conclusions. Large language models are good at producing balanced-sounding evaluation because they have absorbed vast quantities of argumentative text. The result reads like genuine critical thinking but is often a smooth restatement of common positions with no original reasoning. Detecting that difference by eye alone, across a full cohort, is slow and inconsistent.

How Likelihood Scores Actually Work

Modern AI detection does not give you a yes/no verdict. It gives you a likelihood score — a percentage that estimates how likely it is that the text was generated or heavily assisted by AI. GradeOrbit's detection tool returns a score from 0 to 100%.

That probabilistic framing matters enormously. A score of 84% does not prove a learner cheated. It tells you the linguistic patterns in the text — sentence rhythm, vocabulary distribution, the predictability of word choices — are more consistent with AI output than with typical human writing. You then bring your knowledge of the learner, their previous work, and the assessment context to interpret what the score means. The number narrows your attention; it does not make the decision for you.

One Credit vs Three Credit Detection Models

GradeOrbit offers two detection models. The standard model costs 1 credit per piece of work and is suited to routine checks across a class set. The advanced model costs 3 credits and applies a deeper analysis pass — useful when you are escalating a single piece of work for closer scrutiny, or when an internal verifier wants extra evidence before a malpractice meeting.

For Criminology, a sensible pattern is to run the standard model across every learner's controlled assessment at the deadline, then use the advanced model only on the small number of pieces that flag highest. That keeps your credit cost low while concentrating the higher-quality analysis exactly where it matters. The same approach works well for BTEC coursework, which shares Criminology's extended-writing structure.

What to Do When Criminology Work Scores High

A high likelihood score is the start of a conversation, not the end of one. Once a learner's controlled assessment scores above your centre's threshold, do not fill in a malpractice form before doing three things.

First, look at the learner's track record. Does this writing match the style and standard of what they produce in class discussion and timed tasks? Criminology teachers usually have months of evidence to compare against — seminar contributions, earlier drafts, exam responses. A high detection score on work that is wildly out of line with everything else a learner has produced is far more concerning than a high score on work that matches their usual standard.

Second, talk to the learner. Ask them to explain a specific paragraph in their own words. Ask why they chose a particular campaign, theory, or piece of evidence. Genuine writers can always account for their choices. Learners who have leaned heavily on AI usually cannot — and that conversation, recorded in your assessor notes, is often stronger evidence than the score itself.

Third, apply your professional judgment. You are the assessor. The detection score is one data point. Your knowledge of the learner, the assignment brief, and the standards of the qualification matter more than any single number. For more on this, see our guide to using AI detection as professional evidence.

Building a Department Policy Around Likelihood Scores

If you lead Criminology in your school or college, agree a shared threshold with your team before the next controlled assessment window. Some departments use 70% as the trigger for a verifier conversation; others use 85%. The exact figure matters less than the consistency — every learner is treated the same way, and every flagged piece goes through the same evidence-gathering process.

Document the policy and tell learners at the start of the unit that AI detection is part of how you check submitted work. That transparency tends to reduce AI use far more than punishing learners after the fact. We cover this in detail in our piece on writing a school AI academic integrity policy.

What GradeOrbit Will Not Do

GradeOrbit does not store the student work you upload in the solo journey. The text is sent for analysis and the result is returned — no copy is kept on our database or any other service. We also never identify learners by name; students are referred to as "Student 1", "Student 2", and so on. This matters for Criminology departments, because controlled assessments can touch on sensitive real-world cases and should not be sitting in a third-party database.

Try GradeOrbit's AI Detection on Your Next Criminology Deadline

If you have a Criminology controlled assessment window coming up and you want a faster, fairer way to flag work that may have been written with AI, GradeOrbit's detection tool is built for exactly this. You upload the work, choose the 1-credit or 3-credit model, and get a likelihood score back in seconds. You stay the assessor — the tool simply gives you the evidence to make a confident, professional judgment.

Visit the GradeOrbit homepage to create a free account and run your first detection check today.