> 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/more-in-this-section-2/eval-patterns.md).

# Education and EdTech evaluation patterns

Education evaluation patterns — primitive ratio, span rules, GEPA labeling, cadence.

Recommended primitive ratio for education AI: **50/30/20** — deterministic rules, code assertions, judges. Subject-matter accuracy and pedagogical-quality assessment lift the judge share above what other regulated industries use.

## Primitive ratio

| Primitive           | Share | Why                                                                                            |
| ------------------- | ----- | ---------------------------------------------------------------------------------------------- |
| Deterministic rules | 50%   | Direct-answer guards, instructor-final-decision, confidence thresholds, accommodation accuracy |
| Code assertions     | 30%   | Inter-rater agreement, fairness, reading-level, captioning WER                                 |
| LLM judges          | 20%   | Pedagogical scaffolding, alternate-format faithfulness, representation diversity               |

## Pattern table

| Scenario                            | Primary primitive | Specific check                               |
| ----------------------------------- | ----------------- | -------------------------------------------- |
| Tutoring — accuracy                 | Judge             | Subject-matter faithfulness                  |
| Tutoring — scaffolding              | Judge             | Asks-before-tells rubric                     |
| Tutoring — direct-answer guard      | Rule              | No outright answers on protected assignments |
| Tutoring — age appropriate          | Code scorer       | Reading level matches grade                  |
| Grading — inter-rater agreement     | Code assertion    | Cohen's kappa vs. human reference            |
| Grading — demographic fairness      | Code scorer       | Score parity across proxies                  |
| Grading — rubric coverage           | Rule              | Every criterion scored                       |
| Grading — instructor approval       | Rule              | Final grades require instructor sign-off     |
| Curriculum — standards citation     | Rule              | Items cite alignment standard                |
| Curriculum — accommodation accuracy | Code assertion    | Match IEP/504 database                       |
| Curriculum — representation         | Code scorer       | Diversity of examples                        |
| Integrity — confidence threshold    | Rule              | AI never unilateral                          |
| Integrity — disparate impact        | Code scorer       | Group fairness in flagging                   |
| Integrity — evidence citation       | Rule              | Specific evidence required                   |
| Accessibility — faithfulness        | Judge             | Alternate format preserves content           |
| Accessibility — captioning WER      | Code assertion    | Below regulatory threshold                   |

## Span rules

* `student.context.lookup` — grade, IEP/504, language preference
* `standards.cite` — Common Core / NGSS / state / AP alignment
* `rubric.coverage` — every criterion scored
* `accommodation.match` — IEP/504 plan applicability
* `integrity.flag` — confidence and evidence chain
* `instructor.review` — approval state

## GEPA labeling

For each education judge, label ≥ 50 paired examples (more for high-stakes):

* **Pedagogical scaffolding:** Curriculum specialists + classroom teachers; cover Socratic vs. directive responses
* **Subject-matter faithfulness:** Domain experts per subject (math/science/humanities/CS); ≥ 100 for STEM grading
* **Accessibility faithfulness:** Disability-services specialists label
* **Representation diversity:** Equity specialists label

Run [judge optimization](/more-in-this-section-9/judge-optimizations-1.md) with `budget="medium"`. For grading judges that affect student records, run with `budget="heavy"` and hold out 30%.

## Cadence

| Cadence               | Activity                                                            |
| --------------------- | ------------------------------------------------------------------- |
| **Per-deploy**        | CI gate runs scenario suite (rules + assertions)                    |
| **Per-class-session** | Sample tutoring traces; daily summary to teacher                    |
| **Daily**             | Sample production traces across all scenarios                       |
| **Weekly**            | Demographic-fairness audit on grading; integrity-flag overturn rate |
| **Monthly**           | GEPA re-optimization on judges that have drifted                    |
| **Quarterly**         | Standards-alignment refresh; accommodation database refresh         |
| **Annually**          | OCR / state-audit prep; full bias audit                             |

## See also

* [Education scenarios](/more-in-this-section-2/scenarios.md)
* [Education compliance](/more-in-this-section-2/compliance.md)
* [Recipe: GEPA basics](/9.-improve-tune-the-system/gepa-basics.md)