AI Detection Tools for Schools: What SLTs Should Know

AI detection has moved quickly from a concern held by individual English teachers to a question that sits firmly on the SLT agenda. When students can generate convincing written work using ChatGPT or Claude in minutes, schools face a genuinely new challenge — one that individual teachers cannot solve alone. Choosing the right AI detection tool for your school, and embedding it into policy rather than leaving it to chance, is now a leadership decision.

This guide is for headteachers, deputies, and heads of department who want to understand what AI detection actually does, what it cannot do, and how to deploy it fairly across a school.

What AI Detection Actually Measures

The most important thing any school leader needs to understand before adopting an AI detection tool is that no tool can prove, with certainty, that a piece of work was written by AI. What detection tools provide is a likelihood score — a probability, not a verdict.

GradeOrbit, for example, returns a score between 0% and 100%. A score of 85% does not mean that 85% of the essay was written by AI. It means that, based on the statistical patterns in the writing, the tool calculates an 85% probability that AI was significantly involved. That is a meaningful signal. It is not a confession.

This distinction matters enormously for how schools use detection. A likelihood score should inform a conversation with a student — not replace one. Teachers and school leaders still need to exercise professional judgment. The score is one piece of evidence among many: prior work, classroom performance, the student's ability to explain their own writing, and context all matter.

The Fairness Problem That Only SLT Can Solve

Here is the scenario that plays out in schools without a consistent detection approach: one teacher in the English department flags a student's coursework using a detection tool. The student in the next class — same year group, same assessment — is never checked. If the first student faces consequences and the second does not, the policy is not fair. It is not even a policy. It is luck.

Inconsistent detection creates inconsistent outcomes. That inconsistency is not just an integrity risk — it is a safeguarding and equalities risk. If detection is applied more often in some subjects, year groups, or ability sets than others, schools can inadvertently create patterns that disadvantage particular students.

The only way to solve this is at the school level. A senior leader needs to decide which assessments will be screened, how results will be handled, and what the escalation process looks like. Individual teacher discretion cannot substitute for a consistent school-wide policy.

What to Look For in a School AI Detection Tool

Not all AI detection tools are designed with schools in mind. When evaluating options for your school, there are several questions worth asking.

First, does the tool retain student work after processing? Any tool that stores student-generated content creates a data protection concern. Under UK GDPR and the Data Protection Act 2018, schools have obligations around how student data — including written work — is handled and retained. GradeOrbit does not store uploaded student work after analysis. The content is processed and discarded.

Second, how does the tool handle handwritten work? Much of the assessed work in UK secondary schools — exam scripts, in-class essays, controlled assessments — is handwritten. A detection tool that only works on typed or digital text has limited utility for most secondary school teachers.

Third, what model options does the tool offer? GradeOrbit provides two detection modes: a 1-credit standard analysis and a 3-credit in-depth analysis. The in-depth model provides greater nuance and is better suited to borderline cases where professional judgment is critical. Schools can choose which model to use based on the stakes of the assessment.

How GradeOrbit Handles Detection for School Teams

GradeOrbit is built for teachers, but its school account structure is designed for deployment at department or whole-staff level. Rather than individual teachers each managing their own credits, a school account pools credits centrally. This means an SLT or business manager can set a monthly or annual credit allowance, and staff across departments draw from the same pool.

The likelihood score GradeOrbit returns is always accompanied by framing that reinforces professional judgment. The tool does not label work as "AI-generated" — it returns a probability and expects the teacher to interpret it in context. This framing matters when schools face questions from parents or governors about how detection results are used.

For schools that want to understand AI detection more deeply before committing to a school-level approach, our post on how to handle AI detection scores walks through how to interpret results and what follow-up looks like in practice.

Turning Detection Into Policy, Not Individual Habit

The most common failure mode for AI detection in schools is adoption without policy. A few motivated teachers start using a tool, get inconsistent results because they are applying it inconsistently, and the initiative quietly fades. Detection needs to be embedded in your academic integrity framework — not bolted on top of it.

A school AI detection policy should answer at least four questions. Which assessments will be screened? Who decides what happens when a high likelihood score is returned? What is the process for speaking with a student and their parents? And how is the outcome documented?

The DfE's guidance on AI in education, published in 2023, makes clear that schools should have explicit policies governing the use of AI tools in assessment contexts. Detection is part of that picture. If your school's acceptable use policy or assessment policy does not yet reference AI, that is a gap worth closing before detection is rolled out at scale.

Getting Started: What Your School Needs to Sign Up

Setting up GradeOrbit for your school does not require IT involvement or a lengthy procurement process. Schools sign up through a designated signatory — typically a headteacher, deputy, or business manager — using a work email address. Consumer email addresses are not accepted, which helps ensure the account is tied to the institution rather than an individual.

A school's URN (Unique Reference Number) can be provided during sign-up, though it is optional. Once the account is active, the signatory can add staff members with different roles — owner, admin, or teacher — depending on how much access and visibility each person needs.

Credits can be purchased on a monthly or annual basis. Annual billing typically works out more cost-effective for schools that intend to use detection consistently across the year, rather than in bursts around coursework deadlines.

Get GradeOrbit for Your School

GradeOrbit gives school leaders a way to approach AI detection consistently, fairly, and in a way that supports — rather than replaces — teacher judgment. If your school is ready to move from ad hoc detection to a structured approach, GradeOrbit's school accounts are designed to make that transition straightforward.

Visit GradeOrbit to learn more about school accounts and to register your interest.