Built for vocational assessment workflows
- Pearson HN-compatible grading structure
- Evidence-linked criteria decisions
- Quality assurance and Turnitin checkpoints
- Moderation-ready outputs
- Full audit history
Assessment infrastructure for vocational education
Mark vocational assignments in minutes with a full Pearson-ready audit trail.
Built for end-of-day assessors who need defensible decisions fast.
AI-assisted grading that keeps assessors in control, maps evidence against locked criteria, adds QA and Turnitin checks, and delivers moderation-ready decisions by default.
Workflow
From submission intake to audit-ready output
- 1
Upload submission
Submission and assignment brief are linked for assessor ownership.
Intake
- 2
Evidence extraction
Evidence snippets are extracted and structured from learner work.
Extraction
- 3
Criteria mapping
Extracted evidence is mapped to locked Pearson unit criteria.
Mapping
- 4
AI grading
Draft grade decision and feedback are generated for assessor review.
Draft decision
- 5
QA and integrity checks
IQA/IV checks grading logic, assessment evidence, and Turnitin signals.
QA gate
- 6
Audit-ready output
Moderation pack is exported with rationale, QA sign-off, and history.
Final output
Operational fit
Assessor AI behaves like an assessment engine, not a standalone chatbot. It supports the full operational chain that Pearson-style delivery teams run: assessor decision, IQA/IV check, integrity screening, and moderation evidence.
Vocational training providersAwarding bodiesInternal quality assurance teamsIndependent assessors
Positioning
ChatGPT gives an opinion.Assessor AI gives a defensible assessment decision.
Structured evidence mapping, QA checks, Turnitin signals, and audit history are part of the same workflow.
Output preview
Show the result, not just the promise
AI feedback panel
Submission: DEMO-014 · Unit 8
Brief version locked: BTEC-HN-U8-v3
Criteria met: 2/3Needs assessor confirmation: D1.1Ready for QA handoff
P1.1 Safeguarding duties · Mapped evidence
Section 2.1 identifies legal duties and escalation path with role-specific examples.
M1.2 Risk response · Mapped evidence
Section 3.2 compares interventions, but D1.1 needs stronger justification depth.
Suggested assessor feedback
Pass and merit evidence are clear. To reach distinction, expand the rationale for intervention choice with explicit links to case risk factors.
Assessor can accept, edit, or send back for remap before IQA/IV check.
Criteria mapping
Criterion + evidenceOutcome
P1.1 Define safeguarding duties
Section 2.1 + Appendix A
M1.2 Evaluate risk response
Section 3.2 + Case table
D1.1 Justify intervention plan
Section 4.4 + Reflection
QA and Turnitin gate
Assessment decision aligns to mapped criteria
PassEvidence references resolve in the submission
PassTurnitin similarity score 18% vs threshold 15%
ReviewIQA sample flag for moderation
RequiredAudit sequence
- 122:31 Upload logged and file integrity verified
- 222:32 Evidence extraction completed
- 322:34 Criteria mapping snapshot saved
- 422:36 AI grading draft generated
- 522:38 QA + Turnitin check completed
- 622:39 Assessor review accepted and export created
This preview is intentionally synthetic. No student submissions, names, or institutional records are shown on the landing page.
Early access
Assessor-AI is still in active development
We are working with assessment teams to refine the workflow before broader rollout. If you want to test the platform or join the pilot, send a contact request and we will schedule onboarding.
Share your assessment setup and pilot scope. We will review it and contact you with next steps.