🎓 Education AI - Learning Intelligence Systems

Education AI that the teacher,
the parent and the regulator trust

AI systems for K-12, higher-ed and EdTech - adaptive learning, RAG tutor, auto-grading, dropout prediction. Every decision explainable, every model documented. Built for FERPA, COPPA, GDPR-K and IL Privacy Law.

Consultation call
⚠️ Regulated domain

Education AI is not a black box

Every decision needs an explanation - for the regulator, for the user and for the internal auditor.

100%
Explainable Decisions
FERPA + COPPA
compliance
GDPR-K
ready
Audit-aware
design
⚙️ Education AI solutions

End-to-end AI for the sector

🧠

Adaptive Learning

Personalised learning paths grounded in evidence.

  • Knowledge tracing per student
  • Item-response theory grounded difficulty
  • Mastery-based progression
  • Teacher dashboard with reasoning
📚

RAG Tutor

Q&A tutor over your textbooks and curriculum, with citation.

  • Citation back to source page
  • Curriculum-bounded answers (no off-topic)
  • Multi-language (EN / HE / RU)
  • Teacher-tunable answer-strictness
✍️

Auto-Grading

Essay, short-answer and code grading with rubric explanations.

  • Per-rubric-dimension scores
  • Inter-rater agreement reported
  • Plagiarism / AI-generation detection
  • Teacher override + feedback loop
📊

Dropout Prediction

Early-warning system for at-risk students.

  • Multi-signal model (attendance, grades, engagement)
  • SHAP explanations per student
  • Bias monitoring by cohort
  • Counsellor workflow integration
🛡️

Student Privacy

FERPA + COPPA + GDPR-K compliance end-to-end.

  • PII redaction on RAG retrieval
  • Parental consent workflow
  • Right to delete + portability
  • Data residency controls
🔐

Model Governance

Documentation, monitoring and audit of every production model.

  • Model registry per course / cohort
  • Drift detection on engagement signals
  • Champion / Challenger A/B
  • Reportable bias monitoring
📋 Regulation

Built for every regulator in the room

🇺🇸

US: FERPA, COPPA, state-level AI laws

FERPA (Family Educational Rights and Privacy Act), COPPA for under-13, IL Student Online Personal Information Protection Act (SOPIPA) and growing state-level AI-in-schools regulations.

🇪🇺

EU: GDPR Article 8 (minors), EU AI Act, eIDAS

GDPR Article 8 special protection for minors, EU AI Act Annex III high-risk classification for education AI, eIDAS for credentials and qualifications.

🇮🇱

IL: Privacy Protection Law, MoE guidance

Israel Privacy Protection Law 5741, Ministry of Education digital-learning guidance, INCD security baselines for EdTech systems.

📊 Industry benchmarks

What to expect from Education AI that works in production

Most models fail not in the demo - but in production: drift, audit, integration. Here is what the public industry research reports and where our architectural contribution comes in.

🧠

Adaptive learning outcomes

What the industry reports: Adaptive learning systems with knowledge-tracing report 0.3-0.5 effect-size improvement on standardized assessment vs uniform pacing (US DoE What Works Clearinghouse 2024).

Where we come in: Knowledge tracing per student, item-response-theory grounded difficulty, mastery-based progression, teacher dashboard with model reasoning - so the teacher can override.

  • Knowledge tracing per student
  • IRT-grounded difficulty (not heuristics)
  • Mastery-based progression with teacher override
  • Teacher dashboard with model reasoning
✍️

Auto-grading inter-rater agreement

What the industry reports: Per-rubric-dimension auto-grading reports 0.80+ Cohen's kappa with human graders on essay scoring when calibrated on local rubric (ETS research 2024).

Where we come in: Per-rubric-dimension scores, inter-rater agreement reported, plagiarism / AI-generation detection, teacher override + feedback loop - calibrated to YOUR rubric, not vendor benchmark.

  • Per-rubric-dimension scores (not single grade)
  • Inter-rater agreement reported per cohort
  • Plagiarism + AI-generation detection
  • Teacher override feeds back into model
📊

Dropout prediction

What the industry reports: Multi-signal dropout-prediction models achieve 0.80+ AUC for at-risk students by week 6 of term when combining attendance + grade + engagement signals (Educause 2024).

Where we come in: Multi-signal model (attendance, grades, engagement), SHAP explanations per student, bias monitoring by cohort, counsellor workflow integration - actionable, not just predictive.

  • Multi-signal model (not just attendance)
  • SHAP explanations per student
  • Bias monitoring by cohort
  • Counsellor workflow integration

* SLAtech, since 2004. We have worked with universities, K-12 networks and EdTech vendors in 14 countries. Commercial examples available on a consultation call.

Ready to build Education AI that survives parent, teacher and regulator review?

30-minute consultation call - we map the learning use cases, propose an architecture, and quantify ROI.