Trigger: AI may influence learning pathways or educational outcomes.
Skills and Education AI Passport
Approve learning AI
before it shapes pathways.
Structure evidence for AI tutors, skills graphs, recommender systems, education RAG assistants, and workforce-learning platforms.
Sensitive context
Why skills and education AI needs structured evidence.
AI systems influencing learning pathways, skills recommendations, and educational outcomes shape careers and opportunities. Unexplained recommendations, biased skills matching, or poorly grounded RAG systems can limit access to education and work — at scale.
Data categories in scope
People affected
Risk scenarios
What typically goes wrong.
Specific failure modes seen in this sensitive context — without structured evidence.
A skills graph recommender suggests pathways with measurable demographic disparities.
No Group Disparity Analysis Signal Receipts. No subgroup breakdown. Disparate impact undocumented. Procurement cannot evidence fairness review.
An education RAG assistant answers questions using outdated course content.
No Grounding and Response Quality Signal Receipts. Document boundary not configured. Learners receive incorrect course guidance.
A workforce learning AI platform sends learner progress data to a third-party recommendation engine.
No DPA confirmed with recommendation API provider. Learner data leaves platform without structured evidence of legal basis.
An adaptive assessment AI produces results that cannot be reproduced for a learner appeal.
No Model Registry Trace active. No lineage. Past assessment context irretrievable. Appeals process undermined.
Scope
What needs a Passport.
Stakeholder workflow
From trigger to access decision.
Trigger
AI system in scope
Evidence Request
Passport initiated
Review
DPO · CISO · Specialist
Decision
Access condition set
Monitor
Tide sweeps · Renewal
Trigger
AI system in scope
Evidence Request
Passport initiated
Review
DPO · CISO · Specialist
Decision
Access condition set
Monitor
Tide sweeps · Renewal
DPO
“Learner performance and progress data will be processed by an AI recommendation engine.”
Confirm legal basis, DPA, and data minimisation evidence. Review before clearance.
Head of Learning & Development
“A skills matching AI is being procured for workforce development.”
Require Passport with fairness signals and human oversight conditions for high-stakes pathway decisions.
Procurement
“An EdTech or skills platform claims AI-powered personalisation.”
Request structured Passport. Require RAG grounding evidence and data boundary confirmation.
Access decisions
Context Visa conditions.
The access decisions that apply in this sensitive context — and the evidence conditions that produce them.
- Fairness Signal Receipts provided for outcome-influencing features
- RAG grounding evidence present
- Learner data boundary confirmed
- Human educator review gate for high-stakes recommendations
- Fairness evidence missing for outcome AI
- RAG grounding below threshold
- Learner data leaving platform without DPA confirmation
- AI influences formal qualifications, credentials, or grading
- Teacher or assessor must verify before outcome recorded
- Raw learner data exported without legal basis
- No explainability for assessment outcomes subject to appeal
Measurement
Evidence families we can structure.
The measurable evidence categories relevant to this context and the evidence signals they produce.
Data Quality
Completeness, consistency, and freshness of training and inference datasets for skills and learning AI.
Fairness & Subgroup
Subgroup performance and disparate impact for recommendation and outcome-influencing AI.
RAG Grounding
Faithfulness, relevance, and contextual precision of education RAG assistants — without exporting raw course content.
Privacy
Legal basis for learner data processing, DPA status, and data boundary evidence.
Lineage
Dataset and model lineage enabling reconstruction of recommendation or assessment outputs for appeals.
Human Oversight
Evidence that educators or assessors review AI outputs before formal educational outcomes are recorded.
Honest scope
What remains not assessable.
AffectLog does not overclaim. These items require external expertise, regulatory process, or long-term study.
Learning efficacy or educational effectiveness
Whether an AI learning tool improves outcomes requires a controlled evaluation study with appropriate methodology.
Instead: Commission an independent learning efficacy evaluation before deployment at scale.
Credential or qualification recognition
Credential recognition requires awarding body or regulatory authority processes — not an evidence platform.
Instead: Engage the relevant awarding body or regulatory authority for formal recognition processes.
Example
Sample Passport for this context.
SkillsMap Recommender v3
Workforce skills pathway recommendation · L&D Platform
Access conditions
What we will not overclaim
AffectLog provides technical and operational evidence for education and skills AI. We do not claim learning efficacy, educational effectiveness, or credential recognition. We show data boundaries, fairness signals, and what review conditions are required.
Common questions
Questions this context raises.
“Our skills platform uses AI responsibly — we have an ethics policy.”
An ethics policy is a commitment document. AffectLog structures measurable evidence: fairness Signal Receipts, RAG grounding scores, data boundary configuration, and lineage — so DPO and procurement can verify rather than rely on policy statements.
“We don't collect sensitive learner data — only course completion rates.”
Course completion data combined with skills profiles can still reveal demographic patterns and influence opportunity. Even low-sensitivity data categories benefit from structured evidence when AI is used to make recommendations.
Get started
Map every AI tool shaping
learner pathways and skills.
Identify which education and skills AI systems need evidence, which RAG assistants need grounding tests, and which recommendation engines need fairness signals before they influence the next cohort.
AffectLog provides technical and operational evidence. Not learning efficacy claims, educational certification, or regulatory approval.