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 built-in detection tool analyses a piece of work and returns a likelihood score from 0 to 100%, representing how consistent the writing is with AI-generated text. 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.

You choose how much depth you want per check. GradeOrbit offers a faster, lighter analysis for one credit and a more thorough analysis for three credits, so you can run a quick pass over a whole tutor group and then look more closely at the handful of reports that stand out. 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. Our guide on how teachers handle a high AI likelihood score fairly sets out a calm, evidence-based approach that protects both the student and your professional standing.

Keeping Detection Private and Anonymous

A work experience report often names an employer, a placement address and other identifying detail, so privacy matters. GradeOrbit is built privacy-first for solo and team teachers. Before you upload a typed or scanned report, you can use the built-in redaction tool to draw black boxes over the student's name and any identifying information, so work is processed anonymously as Student 1, Student 2, and so on. On the solo and team plans, uploaded student work is never saved to a database — it is used to produce the likelihood score and then discarded, so no reflective write-up sits on a server after you have read it.

Try GradeOrbit's AI Detection Tool

GradeOrbit gives UK teachers a fair, probabilistic starting point for reviewing work experience reports — a 0 to 100% likelihood score you interpret with your own knowledge of the student and the placement they described. It never replaces your judgment; it gives you evidence to apply it consistently and kindly.

Sign up to GradeOrbit and try the built-in AI detection tool on your next set of work experience reports.