How Teachers Interpret Mid-Range AI Likelihood Scores

The 90% score is easy. The 5% score is easy. The 47% score is the one that keeps teachers staring at the screen at half past nine in the evening, trying to work out what to do next. A mid-range AI likelihood score is not a verdict - it is a prompt to think more carefully, and that thinking is where the professional judgement actually happens.

This post is for the teacher who has just opened a piece of Year 10 coursework, fed it through an AI detection tool, and seen a number that sits squarely in the grey zone. The aim here is not to give you a magic threshold. It is to help you turn that uncomfortable number into a fair, defensible next step.

What a Mid-Range Score Actually Means

AI detection is probabilistic. It is not a fingerprint, a lie detector, or a confession. When GradeOrbit returns a score of, say, 45, what it is telling you is that this piece departs from the pupil's own earlier writing on file in some ways but not others - somewhere between clearly consistent with how they usually write and clearly different from it. There are several reasons that might happen, and most of them are not "the student cheated".

A student who has used a spellchecker heavily, written in a deliberately formal register because they were told to, drafted an essay from detailed notes, or simply writes in a clean, structured way may produce text that lands in the middle of the range. Equally, a student who used AI to draft and then rewrote substantial parts in their own voice may also land there. The score alone cannot tell you which is which - and that is the point.

The job of a mid-range score is to flag the piece for a closer human read, not to make the decision for you.

Reading the Score Alongside the Rest of the Evidence

Before you do anything else, put the score down and read the work. Read it as if no number were attached. Then ask the questions you would ask of any piece of work that surprised you:

• Does the vocabulary match what this student has produced in class for you?
• Does the argument structure match their usual planning style?
• Are there topic-specific quirks - the textbook example they always reach for, the misconception they have shown twice this term - present or absent?
• Did they submit a planning document, redraft, or earlier version? Does the final piece show the journey?
• Have you seen them write at this length and complexity in a controlled condition recently?

Each of these is a piece of evidence. The likelihood score is one more piece - not the heaviest one. A mid-range score with three other signals pointing to "this does not feel like their voice" is a different situation from a mid-range score on a piece that otherwise reads like the student you teach.

The Fair Conversation - Curiosity, Not Accusation

If the combined picture leaves you uncertain, the next step is a conversation, not a sanction. The single biggest mistake teachers report is opening with the score. "This came back as 60% AI" puts the student straight into defence mode, and you lose the chance to learn anything useful.

A better opening is curiosity. Ask them to walk you through how they approached the piece. Where did they start? What did they find hardest? Which paragraph took the longest? A student who genuinely wrote the work can usually answer those questions in detail. A student who didn't will often hesitate, contradict themselves, or default to vague answers.

This is not entrapment - it is exactly the conversation a head of department would have at moderation. Frame it as routine, because that is what it should be. Our companion post how to talk to students about AI detection results walks through more of the language and tone for these conversations.

What the Score Is Already Weighing

GradeOrbit's AI detection, part of its school accounts, has no separate everyday-triage-versus-closer-scrutiny mode to reach for when a score sits in the grey zone. Instead, the check is baseline-aware: it compares the submitted piece against that same student's own earlier writing on file, rather than judging it against a generic sense of "AI-like" prose. For a mid-range score, that comparison is often more useful than a second run would be, because it tells you whether this piece is consistent with, or a departure from, the student you already know.

Re-running the same piece will not usually move the number, because the baseline comparison is already built into the one check you get. Where you gain real ground on a mid-range score is not a deeper model - it is more evidence on your side: an earlier piece to compare the voice against, a planning document, or a short supervised in-class write.

Logging the Decision for Moderation and Policy Alignment

Whatever you decide, write it down at the time. A short, dated note in the student's record - score from the first run, score from the second run if you did one, the evidence you weighed, the conversation you had, the conclusion - is worth its weight in gold three weeks later when a parent asks, or six months later when a moderator queries a grade.

This is also where your school's academic integrity policy should anchor your decision. A consistent department-wide threshold for escalation, written down and agreed in advance, takes a huge amount of pressure off individual teachers in the moment. If your school has not yet written one, our post on how to write a school AI academic integrity policy is a useful starting point.

A mid-range score is not a problem to be solved with a single click. It is the start of a professional process. The score gives you a reason to look closer; your judgement, the evidence around the piece, and a fair conversation with the student do the rest.

Try GradeOrbit's AI Detection With a Free School Trial

GradeOrbit's AI detection gives you a score from 0 to 100, checked against that pupil's own earlier writing, with a confidence label, the signals it noticed and a plain-English reasoning paragraph - so you can act on the easy scores quickly and give the grey-zone ones the attention they deserve. It is included with unlimited checks in every school teacher seat. Start a free school trial and see how baseline-aware detection fits into your marking week.