> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/4.2-industry-use-cases/pattern-8.md).

# 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](https://github.com/LayerLens/gitbook-full/blob/main/08-evaluate/guides/bootstrap-judges.md) 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

```python
# Python (SDK)
from layerlens import Stratix
client = Stratix()

pedagogical = client.judges.create(
 name="socratic-vs-give",
 evaluation_goal="Score 1-5: did the tutor guide the student to the answer (5) or hand it over (1)? Penalize answers that bypass the learning step the assignment expects.",
)

age_appropriate = client.judges.create(
 name="age-appropriate",
 evaluation_goal="Content matches the student's declared age band (input.age_band).",
)

pii_redaction = client.scorers.create_code(
 name="ferpa-pii-redaction",
 code="result = {'passed': not contains_student_record_fields(trace)}",
)

trace_eval = client.trace_evaluations.create(
 trace_set={"tags": {"feature": "tutor"}, "sample_rate": 0.005},
 scorers=[pii_redaction.id, subject_answer_key_id],
 judges=[pedagogical.id, age_appropriate.id],
 schedule="daily",
)
```

```typescript
// TypeScript (REST)
const r = await fetch("https://stratix.layerlens.ai/api/v1/trace-evaluations", {
 method: "POST",
 headers: {
 "X-API-Key": process.env.LAYERLENS_STRATIX_API_KEY!,
 "Content-Type": "application/json",
 },
 body: JSON.stringify({
 trace_set: { tags: { feature: "tutor" }, sample_rate: 0.005 },
 scorers: [piiRedactionId, subjectAnswerKeyId],
 judges: [pedagogicalId, ageAppropriateId],
 schedule: "daily",
 }),
});
```

## 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

* [Judges with GEPA optimization](/8.-evaluate-score-the-outputs/judges-1.md) — pedagogical-quality and age-appropriateness
* [Custom code graders](https://github.com/LayerLens/gitbook-full/blob/main/08-evaluate/cookbook/custom-code-scorer.md) — subject-accuracy answer-key check, PII-redaction regex
* [Trace evaluations](/8.-evaluate-score-the-outputs/trace-evaluations.md) — continuous sampled

## Replicate this

**Get started:** [Cookbook: education tutor helpfulness](https://github.com/LayerLens/gitbook-full/blob/main/04-use-cases/industry/education/cookbook/industry-education-tutor.md) is the runnable starter.

* [Industry → Education](/4.2-industry-use-cases/education.md)
* [Concept: Judges](/8.-evaluate-score-the-outputs/judges-1.md)
* [Workflow: Evaluate](/9.-improve-tune-the-system/workflow.md)