How Teachers Detect AI in Work Experience Reports

Detecting AI in work experience reports has crept onto the summer-term to-do list for tutors, PSHE leads and heads of year. Placements cluster at the end of the year, and the reflective write-up that follows is exactly the kind of task a student can produce in seconds with ChatGPT or Claude: describe a week in an office, add a paragraph on "what I learned about teamwork", and a fluent, confident report appears. When a student who rarely writes more than a few lines in class hands you a polished 500-word reflection, you need a fair, consistent way to check before it goes in their record.

This guide looks at how teachers use AI likelihood scores to review work experience reports, why this genre produces so many false positives, and how to keep your own judgment at the centre of any conversation with a student.

Why Work Experience Reports Are Easy to Fake

A reflective report has no mark scheme pulling it in a particular direction and no factual content a teacher can verify at a glance. It is a personal, structured account of a generic experience - and generic, structured, personal-sounding prose is precisely what large language models are best at. A student only has to tell the model where they went and what the placement involved, and it will supply the reflection, the "skills developed" section and the neat concluding paragraph.

There is a real reason to care beyond honesty for its own sake. The point of the write-up is the reflection - the student actually thinking about what the week taught them about work, about themselves, and about the path ahead. A report drafted by AI skips the only part that has any value. Helping a student write their own account is part of the careers and pastoral job, and that starts with an honest read of what is genuinely theirs.

How AI Detection Actually Works

AI detection is probabilistic, not a verdict. GradeOrbit's detection, included on school accounts, compares a new piece of work with that student's own earlier work already on file and returns a score from 0 to 100 for how far it departs from their own writing, with a Low, Medium or High confidence label, the signals it noticed, and a plain-English explanation. It does not prove that a student used AI, and it should never be treated as an accusation on its own. A high score is a strong signal that a report is worth a proper conversation; it is not evidence of misconduct.

GradeOrbit's AI detection is unlimited and included in every school teacher seat - there's no per-check charge and no lighter-versus-deeper analysis to choose between. It compares the report against that student's own earlier writing on file (a student with no earlier work on file is skipped rather than scored, and you are told), so a quick pass over a whole tutor group is already the closer look, not a shortcut you trade off against it. For a reflective task, the score is most useful read alongside everything else you know: the student's classwork, their spoken account of the placement, and the way they talk about it when you ask. Our guide on how AI detection likelihood scores work explains the reasoning behind the number in more detail.

Where False Positives Hide

Work experience reports generate more false positives than most everyday classwork, and it is essential to understand why before you act on a score.

• Template scaffolding. Many schools hand out a report structure - headings for the employer, the tasks, the skills gained, the reflection. Every student converging on the same scaffold produces uniform, formal writing that can read as machine-generated.
• Borrowed corporate language. Students absorb the vocabulary of the workplace ("communication skills", "working under pressure", "professional environment") and repeat it faithfully, which flattens their voice in a way that mimics AI.
• EAL and very able students. A student writing in an additional language, or a strong writer who defaults to a formal register, can score higher without any AI involvement at all.

This is exactly why a high score is a prompt to look, not a conclusion. The most reliable check you have is the student in front of you: a young person who can talk with specific, unscripted detail about who they shadowed, what went wrong on the Tuesday, and what surprised them, is giving you far more than any percentage can.

Handling a High Score Fairly

When a report returns a high likelihood score, treat it as the start of a supportive conversation, not the end of an investigation. Ask the student to walk you through their week - the name of the person they worked with, one thing that did not go to plan, what they would do differently. AI-generated reflections tend to be vague and interchangeable: a placement described in stock phrases, a "lesson learned" that evaporates under a single follow-up question.

Frame it as helping them produce an authentic account, because their own reflection is the thing that actually helps them next - in an interview, a personal statement, or the next placement. Keep a short, factual note of what you discussed and agreed.

Keeping Detection Data Protected

A work experience report often names an employer, a placement address and other identifying detail, so data protection matters. AI detection in GradeOrbit runs on school accounts, under a signed Data Processing Agreement. Because the check compares each report with the student's own earlier writing, pupils' work is held with their real names for the school's chosen retention window. Scans are removed when a run archives (90 days by default, and the school can set anywhere from 7 to 365 days), pupil records are wiped at offboarding, and work is never used to train AI models. Individual teacher accounts don't include detection; there, work uploaded for marking is never saved and students are referred to anonymously as Student 1, Student 2, and so on.

Try GradeOrbit's AI Detection Tool

GradeOrbit gives you a fair starting point when a work experience write-up looks off - a 0 to 100 score showing how far a report departs from the student's own earlier work, which you interpret with your own knowledge of the student and the placement they described. It is included in every school teacher seat, and it never replaces your judgment; it gives you evidence to apply it consistently and kindly.

See how AI detection works for schools and start a free school trial before your next set of work experience reports.