How Teachers Build an AI Detection Routine for Marking Season
Most teachers do not have an AI detection problem so much as an AI detection consistency problem. One week a suspicious essay gets pulled apart paragraph by paragraph; the next, an equally suspicious one slides through because the marking pile was three classes deep and the clock had run out. When AI use is caught unevenly, students learn that detection is a matter of luck rather than process - and that is exactly the lesson you do not want them taking into Year 11 and beyond.
The fix is not to check harder. It is to build a routine: a small, repeatable set of steps that fold AI detection into the marking you already do, so every piece of work passes through the same checkpoint regardless of how tired you are or how tall the pile is. This guide sets out a practical routine for UK secondary teachers heading into a busy marking season.
Why a Routine Beats Spot-Checking
Spot-checking is reactive. You notice something off - a sudden jump in vocabulary, a paragraph that reads more like a textbook than a teenager - and only then do you investigate. The problem is that AI-assisted writing has become good enough that the obvious tells are disappearing. The work that should worry you most is the work that does not announce itself.
A routine flips the logic. Instead of asking "does this essay look suspicious enough to check?", you check everything the same way, every time, and let the signal surface. This is fairer to students, more defensible if a decision is ever challenged, and - counterintuitively - faster, because you are not spending energy deciding what to investigate.
Step One: Set Your Detection Trigger Points
Decide in advance which pieces of work get screened. Trying to detect AI in every single homework task is unsustainable and unnecessary. Pick the high-stakes points where AI use does real damage: coursework drafts, controlled-assessment write-ups, extended-writing assessments, and mock-exam essays produced outside the exam hall. Day-to-day classwork done in front of you needs no screening at all.
Write these trigger points into your department's marking calendar so they are not a personal habit that lives or dies with one teacher. When the whole team screens the same task types, students cannot game the gaps.
Step Two: Understand What the Score Is Telling You
AI detection is probabilistic, not forensic. On a GradeOrbit school account, detection returns a score between 0 and 100 - a measure of how far the new work departs from that pupil's own earlier writing on file, with a confidence label, the signals it noticed and a short reasoning paragraph. It is a triage instrument, not a verdict, and it never replaces your professional judgment.
A score of 90 does not mean "90 percent of this was written by ChatGPT". It means the work differs sharply from how this pupil usually writes. A score of 45 does not mean "45 percent AI" - it means the signal is mixed, which often points to a human draft that has been heavily edited by a tool, or a student deliberately imitating the flat, confident register they have seen AI produce. The number tells you where to look harder. It does not tell you what to conclude.
If you want a deeper walkthrough of the middle band, our guide to how teachers interpret mid-range AI likelihood scores is a useful companion.
Step Three: Pair Every Score With a Human Check
The routine only works if the score is never the last word. For anything above your concern threshold - many teachers set this around 70 percent - run a quick three-point human comparison against the same student's recent classwork: vocabulary range, sentence rhythm, and idea density. Genuine teenage writing grows unevenly and carries the fingerprints of the person who wrote it. AI prose tends to be evenly weighted, confidently argued from the first line to the last, and oddly tireless.
Reading a borderline piece aloud is the single fastest check. Real student writing has peaks and troughs - a strong opening that thins out, an idea repeated because the student forgot they had already made it. If the work reads with suspiciously even stamina, that is worth a conversation, whatever the number on the screen says.
Step Four: Log the Decision in Thirty Seconds
Whenever a score pushes you to investigate, leave a one-line record: date, task, score, what your human comparison showed, and the action you took. This is not bureaucracy for its own sake - it is what turns a gut feeling into defensible professional evidence if a parent or senior leader ever asks how a decision was reached. Thirty seconds now saves an uncomfortable hour later.
On a school account, each detection result also sits on the pupil's profile for as long as the school's retention window allows, so your one-line note and the result itself stay together when someone asks later.
Step Five: Keep It Simple and Repeatable
A routine that takes too long to run is not a routine. On a school account, detection is included in every teacher seat with no per-check charge, so there is nothing to ration across the handful of high-stakes pieces - every check gets the same baseline-aware comparison against that pupil's own earlier writing on file. Run it on a whole class at once: bulk upload the set, set the pages per student, and GradeOrbit matches each cover page to your class list. That is what keeps the whole routine sustainable across a full marking season rather than burning out after the first week.
Try GradeOrbit This Marking Season
If you want to bring the same calm, consistent AI detection routine to every assessment you mark this term, GradeOrbit school accounts are built for exactly that - whole-class checks against each pupil's own earlier writing, included in every teacher seat, with pupils' work held under the school's Data Processing Agreement. See how detection works on a school account and start a free school trial.