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Pattern: AI tutor

Education / EdTech — AI tutor pattern. Pedagogical-quality (Socratic vs. answer-giving), age-appropriateness, learning-outcome correlation.

An EdTech provider operates an AI tutor that helps students through math, reading, and science. The product's value comes from teaching, not answering — a tutor that gives away the answer accelerates the assignment but undermines the learning. COPPA and FERPA frame the regulatory bar; the pedagogical dimension is what differentiates the product.

This pattern shows how to evaluate tutor pedagogical quality alongside accuracy and safety.

What's at stake

Risk dimension
Magnitude
Framework

Learning-outcome harm from answer-giving instead of guiding

Long-term student-outcome impact

Education research

COPPA violation (under-13 users)

Per-violation civil penalties

COPPA

FERPA violation (student records in trace bodies)

Loss of federal funding eligibility, civil penalties

FERPA

Age-inappropriate content exposure

District contract termination, brand damage

District procurement requirements

Accessibility (Section 508 / WCAG)

Procurement disqualification

Section 508 / WCAG 2.1 AA

The evaluation pattern

A multi-criteria evaluation runs against curated tutoring-conversation traces.

  1. Pedagogical-quality judge (GEPA-tuned against ≥50 educator-labeled examples — scored output, "guide vs. give") — penalize responses that hand the student the answer when the assignment expects the student to derive it.

  2. Subject-accuracy scorer — math and science answers verified against a deterministic answer key; language and reading answers checked against rubric criteria.

  3. Age-appropriateness judge (GEPA-tuned against ≥50 examples — scored output) — content matches the age band declared in the user's profile.

  4. PII-redaction scorer (deterministic regex) — no student-record fields appear in any logged span (FERPA).

  5. Reading-level scorer — explanations at or below the configured grade level for the student's age band.

Don't have labels yet? See Bootstrap a judge before GEPA for the week-1 setup.

Continuous trace evaluation: sampled at 0.5% of tutoring sessions, daily. Pedagogical-quality trends visible to the curriculum-design team.

Configuration in code

What you get

  • Direct-answer rate measured per subject and grade band; product-led iteration drives it down over time.

  • Age-appropriateness regressions blocked at the per-student session boundary.

  • FERPA-compliant trace handling enforced by code graders.

  • Auditor-ready evidence for district procurement and state-board reviews.

Stratix capabilities used

Replicate this

Get started: Cookbook: education tutor helpfulness is the runnable starter.

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