How to Handle AI Detection Scores in Student Work

You run a piece of student work through an AI detection tool and it comes back with a score of 84%. Now what? The number sits on your screen and the next steps feel far from obvious. This is the moment that AI detection tools are quietly putting teachers in every week — and it's a moment that requires careful thought rather than a rapid conclusion.

This guide is for UK secondary school teachers who want to understand what AI detection scores actually mean, when a high score is genuinely concerning, when it isn't, and how to build a fair, evidence-based process around detection results. Getting this right matters — not just for academic integrity, but for the welfare of the students in your care.

What AI Detection Scores Actually Mean

The first thing to understand is that AI detection tools do not tell you whether a student used AI. They tell you how closely a piece of text resembles patterns statistically associated with AI-generated output. That is a subtle but critical distinction.

Most tools express this as a likelihood score — a percentage from 0 to 100. A score of 0% suggests the text is almost certainly human-written. A score of 100% suggests the text strongly resembles AI output. The middle ground — anything from roughly 30% to 70% — reflects genuine ambiguity and should be treated with particular caution.

These scores are probabilistic, not definitive. The tool is making an inference based on linguistic and statistical patterns. It is not reading a log of what software was used to produce the text. A high score does not prove AI use. A low score does not prove the work is authentic. Both of these facts matter when you decide what to do next.

When a High Score Does Not Mean AI Was Used

False positives — cases where genuinely human-written work scores highly — are well-documented and worth taking seriously before drawing any conclusions.

Highly Proficient Writers

Students who write with unusual fluency, consistency, and structural clarity can trigger high detection scores. Academic writing at its best shares many qualities with AI output: clear topic sentences, logical sequencing, balanced argument. A Year 13 student who has been coached intensively for A-Level, or who is a naturally gifted writer, may produce work that looks statistically similar to AI-generated text.

EAL Students Who Have Translated or Drafted in Another Language

Students who speak English as an additional language sometimes draft in their first language and translate, either manually or using translation tools. The resulting English can have a formal, slightly over-structured quality that detection tools may flag. This is a particularly sensitive category — treating a high score on an EAL student's work as evidence of AI use without further investigation could be both unfair and damaging.

Heavily Edited Work

A student who has worked on multiple drafts, received significant feedback, and refined their writing over several weeks may produce text that is cleaner and more consistent than their first instinct. Good teaching produces better writing — and better writing can look more like AI output to a statistical model.

Certain Subject Registers

Formal, technical, or highly structured subject areas — law, religious studies, philosophy — require writing that conforms to conventions. A student who has studied the register carefully and is applying it well may produce text that appears machine-like precisely because they have mastered a formal style.

When a Low Score Does Not Mean the Work Is Authentic

The reverse error is equally important. AI detection tools can be evaded — intentionally or unintentionally — and a low score should not be taken as confirmation that a student wrote the work without AI assistance.

A student who pastes AI-generated output and then edits it substantially — changing vocabulary, adding personal anecdotes, restructuring sentences — will often produce work that scores much lower. The more editing they do, the less the text resembles raw AI output. Similarly, students who use AI to generate ideas or an outline, then write the actual prose themselves, may produce work that scores very low while still having used AI in a meaningful way.

This does not mean you should treat all low-scoring work with suspicion. It simply means that a low score is not a clean bill of health, and that your own knowledge of the student remains your most powerful tool.

A Decision Framework for Teachers

Rather than treating a single score as a trigger for action, a more robust approach looks at the full picture. Here is a practical framework for working through detection results.

Step 1: Contextualise the Score Against Your Knowledge of the Student

Before doing anything else, ask yourself: does this piece of work feel consistent with what this student normally produces? If you have previous examples of their writing — class exercises, rough drafts, timed in-class work — compare them. A dramatic step up in quality, fluency, or structural sophistication is worth noting regardless of the detection score. Conversely, if the score is high but the writing is entirely consistent with what this student always produces, that is significant context.

Step 2: Look for Linguistic Signals in the Text Itself

AI-generated writing tends to show certain patterns: unusually even sentence length, a lack of genuine personal voice, over-reliance on hedging phrases, and a kind of generic competence that hits the right notes without any authentic personality. These signals are worth looking for — not as proof, but as additional data points that either add to or reduce your concern.

Step 3: Consider the Submission Context

When was the work submitted? Was it handed in late at night after the deadline? Is there a pattern with this student of last-minute submissions that are unusually polished? These contextual factors matter. A piece of work that arrives suspiciously close to the deadline and scores highly on AI detection, from a student who typically struggles, represents a stronger case for further investigation than the same score from a confident writer who submitted early.

Step 4: Have a Conversation Before Taking Any Action

If your concern persists across multiple indicators, the most productive next step is a short, non-accusatory conversation with the student. Ask them to talk you through their argument, explain where their evidence came from, or write a paragraph on the same topic in class. A student who genuinely wrote the work will be able to discuss it. A student who submitted AI-generated content will often struggle to explain their own ideas in any depth, or will give responses that don't match the sophistication of what they submitted.

Step 5: Follow School Policy

Before taking any formal action, check your school's academic integrity policy. Many schools are still developing their approach to AI use, and there is wide variation in how AI assistance is classified. Some schools treat submitting AI-generated work as equivalent to plagiarism; others have a more graduated approach focused on education. Follow your policy carefully, document your evidence, and involve a senior colleague if the case is complex.

How GradeOrbit's AI Detection Tool Supports This Process

GradeOrbit includes a built-in AI Detection feature designed with the classroom context in mind. You can submit student work as pasted text, an uploaded image, or a scanned document. The tool returns a likelihood score from 0 to 100%, a confidence label (Low, Medium, or High) indicating how certain the model is, a list of detected linguistic signals that contributed to the score, and a short reasoning paragraph summarising the overall assessment.

The tool is available in two modes: a faster option using 1 credit for quick checks, and a more capable option using 3 credits for cases where you want a deeper analysis. Your model preference is saved between sessions.

Importantly, GradeOrbit does not store student work. The content is sent to the AI model for analysis and then discarded — no student text is retained on our servers. We recommend redacting any identifying information before submitting work, just as you would for any AI-assisted process.

If you'd like a broader introduction to how AI detection tools work, our guide on AI detection for teachers covers the fundamentals in more depth.

Try GradeOrbit's AI Detection Feature

AI detection scores are one piece of evidence among many. Used alongside your professional knowledge of the student, a careful reading of the text, and a direct conversation where needed, they can play a useful role in maintaining academic integrity — without putting students at unfair risk.

GradeOrbit's detection tool is built directly into your dashboard, ready to use with any text, image, or document upload. Try GradeOrbit today and see how it fits into your existing approach to academic integrity.