Education-Specific Standards
Certification Overview
APAE certification standards are designed specifically for AI in education—addressing the unique privacy, safety, and transparency requirements for systems used with minors.
The APAE Certification Mark
Trusted by Schools Across Europe
When you see this mark, you know the vendor has met rigorous standards for student data protection, AI safety, and regulatory compliance. It's your signal that an EdTech product is safe for European classrooms.
APAE Certification Standards
Our standards address six critical areas for AI in education. Each area has specific requirements that vendors must meet for certification.
Student Data Protections
Comprehensive safeguards for student personal data throughout the AI lifecycle.
- Data minimization—collect only what's necessary for educational purposes
- Purpose limitation—data used only for stated educational goals
- Retention limits—clear policies on data deletion after use
- Parental access rights—guardians can view and request deletion
- Cross-border transfer controls—data stays within approved jurisdictions
- Encryption at rest and in transit for all student data
Teacher-in-the-Loop Requirements
Human oversight for all significant AI decisions affecting students.
- Automated decisions flagged for teacher review before action
- Teachers can override AI recommendations at any time
- No fully autonomous decisions on grades, placement, or discipline
- Clear escalation paths when AI confidence is low
- Training materials for educators on AI system limitations
- Dashboard visibility into AI decision factors
Age-Appropriate Transparency
Clear explanations tailored to students' developmental stages.
- Student-facing explanations written at appropriate reading levels
- Visual indicators when AI is being used vs. human input
- Age-appropriate privacy notices for different grade levels
- Parent/guardian communications in accessible language
- No dark patterns or manipulative design for minors
- Clear labeling of AI-generated content
Bias, Fairness & Accessibility
Testing to ensure AI works equitably for all learners.
- Bias testing across protected characteristics (gender, ethnicity, disability)
- Performance parity across different student populations
- Accessibility compliance (WCAG 2.1 AA minimum)
- Support for diverse learning needs and styles
- Language and cultural sensitivity testing
- Regular fairness audits with documented outcomes
Content Safety
Safeguards for AI-generated outputs in educational contexts.
- Content filtering appropriate for educational settings
- Harmful content detection and blocking
- Age-appropriate content boundaries enforced
- No generation of inappropriate material for minors
- Misinformation and accuracy controls
- Human review process for edge cases
Logging & Audit Requirements
Comprehensive records for accountability and incident response.
- Immutable logs of AI decisions affecting students
- Audit trail accessible to authorized school administrators
- Retention of logs for regulatory compliance periods
- Incident logging with timestamp and context
- Performance metrics tracked over time
- Data access logs for security monitoring
Certification Level Comparison
Choose the certification level that matches your product's risk profile and capabilities.
Level 1
AI & Privacy Confirmed
Low-Risk EdTech
Level 2
Responsible AI Verified
Mature EdTech Vendors
Level 3
High-Risk Compliant
Gold Seal
| Data Protection | |||
| GDPR-compliant data handling | |||
| Data minimization practices | |||
| Enhanced parental consent flows | |||
| Cross-border transfer controls | |||
| Third-party data sharing audit | |||
| Human Oversight | |||
| Teacher override capability | |||
| Decision flagging for review | |||
| No autonomous high-stakes decisions | N/A | ||
| Real-time intervention controls | |||
| Full audit dashboard for educators | |||
| Transparency | |||
| AI usage disclosure | |||
| Age-appropriate explanations | |||
| Decision factor visibility | |||
| Model card / system documentation | |||
| Real-time explainability | |||
| Fairness & Safety | |||
| Basic bias testing | |||
| Content safety filters | |||
| Comprehensive fairness audit | |||
| Accessibility compliance (WCAG) | |||
| Third-party fairness validation | |||
| Logging & Monitoring | |||
| Basic activity logging | |||
| Decision audit trail | |||
| Performance monitoring | |||
| Continuous compliance monitoring | |||
| Real-time anomaly detection | |||
Full requirement
Basic / partial
Not required
Documentation Required from EdTech Vendors
Vendors seeking certification must provide comprehensive documentation across these areas. Requirements scale with certification level.
System Documentation
- •AI system architecture and data flow diagrams
- •Model training data sources and methodology
- •Algorithm explanation and decision logic
- •Third-party component and API inventory
Privacy & Compliance
- •Data Protection Impact Assessment (DPIA)
- •Privacy policy specific to educational use
- •Data processing agreements template
- •Cross-border data transfer mechanisms
Safety & Testing
- •Bias and fairness testing methodology and results
- •Content safety measures documentation
- •Accessibility audit results (WCAG compliance)
- •Penetration testing and security audit reports
Governance & Operations
- •Human oversight procedures and escalation paths
- •Incident response plan for AI failures
- •Staff training materials and certifications
- •Ongoing monitoring and update procedures
Ready to Get Certified?
Learn about the certification process and begin your application.