Academic integrity software: what to look for

Academic integrity software is often bought in reaction to a difficult incident. That is understandable, but it can lead to a tool being asked to solve a policy, assessment-design and staff-training problem on its own. This matters now because schools are moving from a simple ban-or-allow argument towards clearer evidence of learning. A useful academic integrity software approach should help a teacher ask a better question, not pretend it can settle a case on its own. That is the promise of this guide: a practical route from uncertainty to a fair, explainable next step.

TL;DR: start with the assignment rules, look at the work in context, check the writing process where you have it, and speak to the student before treating a signal as a conclusion. The details below show what that looks like in a busy classroom.

Start with the question you actually need answered

Separate the jobs. Similarity checking compares a submission with sources it can access. Text classification estimates whether prose resembles generated text. Process tools capture how work entered a writing space. Policy and teaching explain what students may do and why. A coherent integrity approach uses the right combination, with a person responsible for interpreting each signal.

A sound academic integrity software workflow is deliberately modest. It records what happened, distinguishes an observation from an inference, gives the learner a chance to explain, and keeps a short factual note. That sequence protects a conscientious student as much as it helps a teacher investigate a genuine concern. It also makes handover to a head of department far less fraught.

Before opening another tab, write one sentence that names the concern in neutral language. For example: “I need to understand how this conclusion was developed.” That is much better than “I need to prove this is AI”. The first sentence invites evidence; the second invites confirmation bias.

Choose evidence that fits the decision

Not every assignment deserves the same level of scrutiny. A low-stakes homework task may need a quick formative conversation. Controlled assessment or a final project may need a documented route under the centre policy. The table is a simple way to match the academic integrity software check to the consequence rather than making every flag feel like an emergency.

A practical route through the work

Map the journey from assignment creation to resolution. At creation, staff declare permitted AI use and the acknowledgement format. During drafting, students can see the rules. At submission, the system records only the data it needs. In review, a teacher sees context and can invite an explanation. In escalation, leaders receive a factual record and apply the same policy to every learner. If a proposed product leaves a gap in that journey, list the human process that will fill it.

Keep the review narrow. Look for a change that can be described without guessing at motive: a large paste, a missing source trail, an abrupt change in terminology, or an answer the student cannot unpack. Then ask about that point. A student may have a perfectly ordinary explanation, such as drafting offline, using accessibility software, or receiving permitted feedback. Good process makes room for it.

In Learnaway, the relevant evidence is process data: session length, typing and paste events, pauses and focus changes. It does not read the words a learner types. That is why its output is best used alongside the teacher's knowledge of the class and the assignment brief. See how Learnaway works and the teacher workflow for the exact boundaries.

What current guidance says

JCQ's 2025 guidance puts centre arrangements, clear guidance and investigation ahead of any particular technology. UNESCO's 2025 report frames AI in education as a human-centred and rights-based challenge, including equity, privacy and safety. That is why an integrity platform should be evaluated as part of a school system rather than an automated referee.

For a current external check, read the Department for Education's AI guidance and the relevant assessment guidance before changing a school rule. They are more useful than a vendor headline because they describe responsibilities, not a product claim. In the same 2025 Ofsted work, Chief Inspector Sir Martyn Oliver said schools need to “manage the risks” alongside AI's potential.

The common thread is professional judgement. Jisc's assessment and academic-integrity training explicitly covers the limitations of AI detection, authentication and responses to concerns. That is a helpful corrective to the idea that a percentage score is enough. It is not. A percentage is at most a prompt to look more carefully.

Make the route visible before a concern occurs. Students should know what evidence a teacher may consider, who makes the decision, how they can explain a legitimate workflow and when a formal policy applies. Clear process is not administrative clutter. It is the difference between a learning conversation and an unexpected accusation.

Illustrative classroom scenario

A trust initially plans to use a plagiarism product to deal with AI concerns. In a pilot, leaders discover that similarity reports help with copying but say little about original-looking generated text. They add a clear AI acknowledgement field and a process-aware writing option for selected tasks. The important change is not the number of tools. It is that each tool now has a stated purpose and a named reviewer.

The useful detail is not the number on a dashboard. It is whether the teacher can say, calmly and accurately, what they saw and what they asked next. That leaves space for a student to demonstrate understanding, correct a misconception, or disclose help they did not realise needed acknowledgement. It also means a genuine issue can be escalated with a clear record rather than a hunch.

Common mistakes to avoid

Avoid collecting an entire student writing history because it might be useful later. Data minimisation is not a nice-to-have. Avoid setting an automatic sanction threshold. And avoid a procurement exercise led only by IT; classroom, safeguarding and data-protection colleagues need a real voice.

Do not quietly change the rules after students have started. Do not put a detector score in the gradebook as though it were a mark. Do not collect more behavioural or personal data than the decision needs. The ICO's 2026 review covered audits of 28 widely used edtech providers and stressed that children need to be able to trust the tools schools adopt. Read the ICO statement before making data collection the default.

Turn the finding into a next step

Use this article at consideration stage to build requirements before you see a sales demo. At decision stage, ask each supplier to explain a false-positive appeal, a deletion request and the exact evidence a teacher would see.

If you want to try a process-first approach, create one low-stakes assignment, tell students exactly what is recorded, and compare the timeline with work you already know well. Learnaway's free assignment flow is designed for that first small test. For a wider rollout, use the pricing guide and trust information to involve the people responsible for teaching, data protection and safeguarding early.