AI Assessment Integrity Audit
Independent review of candidate identity, unauthorised AI assistance, proxy testing, assessment authorship and evidence provenance across AI-enabled assessment and interview processes.
Explore the AI Assessment Services hub
Why this matters now
AI tools are now woven into how candidates prepare for, and sometimes complete, assessments and interviews. That creates a question employers increasingly cannot avoid: does a result reflect the named candidate’s own identity, effort and reasoning, or something else — a generative AI tool, a coached response, or a substitute test-taker?
Rob Williams Assessment treats this as an integrity and evidence question, not just a technology question. A process can look modern and still fail if identity is unverified, authorship is unclear, monitoring is weak, evidence is not traceable, or flagged cases are never properly investigated or reviewed.
Where integrity risk shows up
- Candidates using generative AI to draft or coach real-time responses beyond permitted use
- Uncertainty over whether the person completing an assessment is the person being evaluated
- Written work or portfolios that may not reflect the candidate’s own reasoning
- Unmonitored browser tabs, secondary devices or hidden assistance during remote testing
- Weak or incomplete evidence trails when a result is challenged
- Inconsistent, undocumented human review when a concern is raised
What the audit covers
Candidate identity
Verifying that the person completing the assessment is the person being evaluated, from registration through to submission.
Unauthorised AI assistance
Identifying where generative AI tools may have shaped responses beyond what the assessment permits.
Proxy testing
Reviewing safeguards against a substitute completing all or part of an assessment on a candidate’s behalf.
Assessment authorship
Establishing whether submitted answers, written exercises or portfolios genuinely reflect the candidate’s own reasoning.
Browser & device monitoring
Assessing technical controls covering tab activity, secondary devices, screen sharing and remote proctoring signals.
Evidence provenance
Reviewing how timestamps, response patterns and session logs are captured, stored and traced back to source.
Test security
Evaluating item exposure risk, content leakage and protection of proprietary scenario and scoring material.
Integrity investigations
Reviewing the process for flagging, escalating and formally investigating suspected integrity breaches.
Human review & appeals
Ensuring candidates flagged by automated systems receive fair, documented human review and a clear route to appeal.
Public-facing methodology note
Example application for a FTSE 100 employer
Assessment example
A FTSE 100 employer notices unusually polished, uniform responses across a remote graduate assessment window. RWA reviews identity verification, device monitoring and evidence logs, then designs a proportionate flagging and investigation pathway.
Development example
The findings inform assessor training, candidate communications on permitted AI use, and a documented appeals process — so future flags are handled consistently and defensibly.
How this connects to the RWA AI Assessment Services hub
This audit sits alongside RWA’s wider AI assessment work. It is the broader integrity layer that interview-specific, defensibility and validation reviews connect into.
| Related service | Why it matters |
|---|---|
| AI Interview Integrity Audit | Focuses specifically on interview question design, probing quality and scoring calibration. |
| AI Hiring Defensibility Audit | Reviews whether an AI-enabled hiring system measures the right thing, fairly and explainably. |
| AI Assessment Governance Audit | Reviews ownership, oversight and version control of the assessment system itself. |
| AI Assessment Validation Playbook | Sets out the psychometric validation layers behind a defensible AI-enabled assessment. |
| Graduate AI Simulations | Measures candidate judgement and AI-output evaluation directly, reducing reliance on unverifiable self-reported evidence. |
Wider AI readiness and workforce context
Assessment integrity is not only a technical control issue — it is also a capability and culture issue. RWA supports employers with psychometric assessment, integrity and governance reviews and defensible talent diagnostics. Mosaic.fit supports workforce AI capability measurement, while SchoolEntranceTests.com extends AI literacy and judgement development into education settings.
For wider context, readers may also review the NIST AI Risk Management Framework, the OECD AI policy observatory, and background on psychometrics.
Discuss an assessment integrity review
Rob Williams Assessment can review existing AI-enabled assessment and interview processes, design integrity safeguards, or build an investigation and appeals pathway for flagged cases.
Frequently asked questions
What is AI assessment integrity?
It is the extent to which an assessment result genuinely reflects the named candidate’s own identity, effort and reasoning, rather than unauthorised AI assistance, a proxy test-taker, or unverified evidence.
How is this different from the AI Interview Integrity Audit?
The Interview Integrity Audit focuses specifically on interview question design, probing and scoring. This audit is broader and covers identity, authorship, proxy testing, device monitoring, evidence provenance and investigations across an organisation’s full assessment estate.
Can this run alongside a Hiring Defensibility Audit?
Yes. Defensibility review focuses on whether an assessment measures the right thing fairly. Integrity review focuses on whether the evidence collected can be trusted. Many organisations commission both together.
Does this require access to our AI vendor’s system?
Not necessarily. An initial review can be conducted from documentation, workflow evidence and process walkthroughs. A deeper technical review may involve vendor engagement where access is available.
What happens if evidence of a breach is found?
The audit sets out a proportionate investigation and human review pathway, so any flagged case is handled consistently, documented properly, and gives the candidate a fair route to respond or appeal.