> 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/scenarios.md). # Education and EdTech scenarios Education and EdTech scenarios — tutoring, grading, content generation, integrity, accessibility. Industry patterns, not customer case studies. Five canonical scenarios where K-12, higher-ed, and professional learning organizations use Stratix evaluation to gate AI features. ## 1. AI tutoring and homework help **Setup:** Student asks the tutor for help on a problem. AI scaffolds the solution, asks Socratic questions, surfaces relevant course material — without giving the answer outright in academic settings that disallow it. **Why it's hard:** * "Help" without "doing the assignment" is a fine line * Subject-matter accuracy must be near-perfect — wrong math/physics/grammar imprints lasting misconceptions * Age-appropriate language across grade levels **Stakes:** * Misconception cost compounds across the rest of the student's learning trajectory * Academic integrity policies of partner institutions vary * COPPA / FERPA / state student-data laws are unforgiving on under-13 audiences **Stratix evaluation criteria:** | Aspect | What you measure | Pattern | | -------------------------- | ----------------------------------------------- | ---------------------------------- | | Subject-matter accuracy | Correct facts, correct math | Deterministic + faithfulness judge | | Pedagogical scaffolding | Asks before tells, guides before solves | Judge against rubric | | Age-appropriateness | Reading level matches grade | Code scorer | | Direct-answer guard | No outright solutions for protected assignments | Hard rule | | PII / private-info hygiene | No surfacing of other students' data | Hard rule | ## 2. Automated grading and feedback **Setup:** AI grades short answers, essays, and code submissions; produces score and detailed feedback. Instructor reviews; AI doesn't finalize grades unilaterally. **Why it's hard:** * Grading bias by topic, dialect, language background * Format vs. substance — verbose-but-wrong should not score above terse-and-correct * Rubric application requires nuance models often lack **Stakes:** * Grade-level disparate impact triggers civil-rights and Title VI exposure * Student-data privacy bound by FERPA + state laws * Higher-ed: regional accrediting bodies scrutinize AI in assessment **Stratix evaluation criteria:** * **Inter-rater agreement** — AI scores vs. human-graded reference set; track kappa * **Demographic-fairness scorer** — Score-distribution parity across protected proxies * **Rubric-coverage rule** — Every score touches every rubric criterion * **Instructor-final-decision rule** — Hard rule: grades not posted until instructor approves * **Plagiarism / generative-AI detection caveat** — Output cannot accuse without strong evidence threshold ## 3. Curriculum and content generation **Setup:** Teachers/instructional designers ask the AI to produce lesson plans, problem sets, reading lists, accommodations. **Why it's hard:** * Standards alignment (Common Core, NGSS, state standards, AP) must be precise * IDEA / 504 accommodation accuracy is a legal obligation * Bias and representation in examples / readings **Stakes:** * Out-of-standard content drives test-prep failures * IDEA/504 errors trigger OCR investigations and consent decrees * Adoption decisions in K-12 districts are public; political controversy travels fast **Stratix evaluation criteria:** * **Standards-citation rule** — Every produced item cites its alignment standard * **Accommodation accuracy** — Code assertion against IEP/504 plan database * **Representation diversity scorer** — Examples and readings show breadth * **Reading-level scorer** — Age-band appropriate * **Editor-review gate** — Hard rule for adopted curriculum vs. exploratory drafts ## 4. Academic-integrity and AI-detection assistance **Setup:** AI flags submissions that appear AI-generated, plagiarized, or off-pattern for the student. Surfaces evidence; instructor adjudicates. **Why it's hard:** * AI-detection accuracy is famously unreliable; false-positive cases publicized * Disparate impact on non-native English writers is well-documented * A wrongful integrity accusation can derail a student's career **Stakes:** * Title VI and ADA exposure for biased detection * Class-action exposure when accusations are statistically unreliable * Student wellness — wrongful accusations have produced documented mental health crises **Stratix evaluation criteria:** * **Confidence-threshold rule** — Hard rule: AI never makes the determination unilaterally * **Disparate-impact monitoring** — Group fairness across language and demographic proxies * **Evidence-citation rule** — Every flag cites specific evidence (matched passages, anomaly indicators) * **Appeal-overturn rate** — Drift signal; high overturn rate triggers retraining * **Student-due-process rule** — Output includes standard-appeal-language disclosure ## 5. Accessibility and accommodation **Setup:** AI produces alternate-format materials (large print, audio, simplified text), captioning, translation, image descriptions for students with disabilities or LEP. **Why it's hard:** * Quality must match the original — paraphrase loses content * ADA + Rehabilitation Act §504 + IDEA + state law obligations * Real-time captioning latency / accuracy in lecture settings **Stakes:** * OCR investigations following accessibility complaints * Settlement consent decrees common for major institutions * Inadequate captioning specifically a long-running litigation theme **Stratix evaluation criteria:** * **Faithfulness judge** — Alternate format preserves all content * **Captioning WER** — Word Error Rate ≤ regulatory threshold (institution-set) * **Translation faithfulness** — Per-language judge runs * **Latency rule** — Real-time captioning under defined latency SLA * **Image-description completeness** — Required visual elements covered ## See also * [Education eval patterns](/more-in-this-section-2/eval-patterns.md) * [Education compliance](/more-in-this-section-2/compliance.md)