AI Automatic PEP Sign-Off with Professional Dip Testing
Confidence through intelligence. Automatically validate Personal Education Plans against your Virtual School's benchmarks, then verify quality through random professional review.
How Dip Testing Works
01
Automatic Random Selection
The system automatically selects a random sample of PEPs that have been signed off by the AI system. Sample size is configurable by your Virtual School.
02
Review Against Benchmarks
Professionals review each sampled PEP against your Virtual School's established quality benchmarks and approval criteria.
03
Confidence Verification
Rate your satisfaction with the quality assurance results. Mark as approved or flag for improvement to refine the AI model.
04
Continuous Improvement
Feedback loop helps the AI system learn and improve its QA accuracy over time, ensuring consistent standards.
Dual Layer QA Process
AI Automatic Sign-Off
The AI system automatically validates every PEP submission against your configured benchmarks in real-time.
- Instant validation — no manual delays
- Consistent application of criteria across all submissions
- Identifies gaps before sign-off
Professional Dip Testing
Virtual School professionals randomly sample and review signed-off PEPs to verify the AI is meeting your quality standards.
- Build confidence in the AI system
- Verify benchmarks align with your approval criteria
- Provide feedback to improve AI performance
Your Benchmarks. Your Standards.
Configurable Quality Criteria
Managed Through Admin Tools
Set and manage all quality benchmarks directly within the eGOV Cloud administration interface. Easily adjust thresholds, add cohort-specific criteria, and refine standards as your authority's needs evolve.
Example:
- • CLA cohort: Minimum 95% attendance, 3+ educational targets required
- • CiN cohort: Minimum 90% attendance, 2+ targets minimum
- • EHCP cohort: Outcome-focused targets with transition planning
Why Dip Testing Matters
Assurance
Know the AI is consistently meeting your quality standards without reviewing every single PEP.
Accountability
Provide evidence to inspectors and stakeholders that your sign-off process is rigorous and evidence-based.
Improvement
Use feedback from dip tests to refine benchmarks and continuously improve the AI model's performance.
Ready to streamline PEP quality assurance?
Configure your benchmarks, let AI validate submissions, and maintain confidence through professional dip testing.