{"resourceId":"osse-dc-lea-ai-model-policy-staff-use-202609","versions":[{"version":"external-48f280f625e7ba2d59184e70d126d9fae9d5cdb8574ee9c3c02338c39a20b9ca","resource":{"id":"osse-dc-lea-ai-model-policy-staff-use-202609","title":"OSSE releases DC LEA AI Model Policy for Staff Use with stoplight prohibitions on discipline, teacher eval, and IEP eligibility","organization":"Office of the State Superintendent of Education (OSSE), District of Columbia","sector":"K-12 state education agency / LEA guidance","geography":"Washington, D.C., United States","publishedAt":"September 2026 (model policy booklet); press release September 1, 2026","publicationDate":"2026-09-01","eventDate":"2026-09-01","sourceName":"OSSE LEA AI Model Policy for Staff Use (PDF)","sourceLabel":"State education agency model policy (voluntary guidance)","sourceUrl":"https://osse.dc.gov/sites/default/files/dc/sites/osse/page_content/attachments/LEA%20AI%20Model%20Policy%20Booklet%20Sept%202026.pdf","evidenceClass":"standards-guidance","outcomeClass":"emerging","topics":["governance-procurement","data-security","accessibility-workforce","operating-model"],"finding":"OSSE’s September 2026 Model AI Policy for Staff Use gives DC LEAs a customizable stoplight: Red bars AI for physical/biometric surveillance, student discipline decisions, teacher performance evaluations, and IEP/Section 504 eligibility determinations; Yellow allows limited use with enhanced oversight (e.g., drafting IEP language, reviewing/grading student work, device monitoring); Green covers lesson planning, tutoring plans, communications, and logistics with human review. Staff must use only approved enterprise tools for PII, complete training before use and renew annually, and keep humans accountable for outputs. The policy explicitly does not set student-use or procurement rules.","sledRelevance":"A transferable SEA staff-use template for districts that need clear prohibited vs. conditional AI use cases, enterprise-tool mandates, and FERPA/COPPA/CIPA/IDEA-aligned vendor expectations without waiting for binding statewide student-AI rules.","evidence":"Opened the September 2026 OSSE PDF booklet end-to-end (executive summary, guiding principles, stoplight tables, Red/Yellow/Green narratives, training and procurement recommendations). Cross-checked with OSSE’s Sept 1, 2026 press release describing the same Red/Yellow/Green categories and 45% staff-policy baseline from the LEA survey.","architectureImplications":"District stacks need an approved enterprise AI allowlist, runtime blocks on consumer GenAI for staff PII workflows, and feature flags that can disable unauthorized AI features inside otherwise-approved products (discipline analytics, eval scoring, eligibility recommenders).","governanceImplications":"Boards/LEAs can adopt the stoplight as local policy with community customization; separate student-use and procurement policies are still required. Annual policy reassessment is recommended as capabilities change.","securityPrivacyImplications":"PII use limited to enterprise tools meeting FERPA, COPPA, CIPA, IDEA, HIPAA, and DC Protecting Students Digital Privacy Act; procurement guidance calls for NIST-aligned controls, MFA, encryption, incident response, and bans on unauthorized consumer-grade tools.","caveats":"Voluntary model policy, not legal advice and not mandatory adoption. Staff-only scope—student GenAI and tool procurement remain local LEA work. Evidence of instructional effectiveness is acknowledged as limited (cites Stanford SCALE 2026 review).","streamIds":["k12"],"roles":{"sales":"Sales framing: DC just published a staff-use floor that buyers will copy. Lead with enterprise-only PII paths, annual AI literacy training, and hard Red prohibitions on discipline, teacher evaluation, and IEP/504 eligibility—not chatbot demos. Ask whether your product can be configured so those high-stakes features are off by default and auditable. Position customization of the stoplight as the engagement, not a one-size student GenAI rollout.","engineering":"Engineering framing: map every feature to Red/Yellow/Green before enablement. Architecture must enforce approved-tool SSO, block consumer endpoints for staff PII, and prevent AI scoring of teacher evaluation or student discipline risk. Yellow grading/IEP-draft features need mandatory human-accept steps and logs. Prove that unauthorized AI features inside suites can be disabled without ripping out the core LMS or SIS.","delivery":"Delivery framing: implementation is policy localization plus training ops. Facilate LEA customization workshops, stand up the enterprise allowlist, schedule pre-use and annual AI literacy courses, and tabletop Red-category misuse. Acceptance means staff cannot complete a Red workflow in production tools and Yellow outputs show human sign-off before acting on students or personnel."},"roleDetails":{"sales":{"customerProblem":"LEAs lack a clear staff AI use map and fear high-stakes misuse (discipline, eval, IEP eligibility) and consumer-tool PII leakage.","stakeholders":["State/SEA leaders","LEA superintendents","CTOs","Special education directors","HR/talent","School board counsel"],"discoveryQuestions":["Which staff AI uses are Red-prohibited in your draft policy?","Can every PII workflow be forced onto approved enterprise tools?","Is annual AI literacy training tracked before tool access?","Do teacher-eval or discipline modules include AI features you can disable?"],"valueHypothesis":"Vendors that ship stoplight-aligned controls and enterprise DPAs will win SEA/LEA staff-AI readiness work faster than consumer GenAI embeds.","potentialEngagement":"4–6 week OSSE-stoplight localization: gap map current tools to Red/Yellow/Green, draft LEA policy text, and produce an allowlist/DPA checklist.","unsupportedClaims":["That adopting the model policy improves student learning","Guaranteed legal safe harbor from FERPA/COPPA claims","That staff-only policy covers student GenAI risk"]},"engineering":{"fit":"Fit for LEAs that can centralize enterprise AI entitlements; poor fit for unmanaged consumer chatbot use on staff devices.","architectureIntegration":"IdP groups → approved AI gateway → per-feature policy flags; separate paths for Yellow grading/IEP-draft with human-accept queues; block Red analytics endpoints.","prerequisites":["Authoritative staff AI tool inventory","Enterprise contracts with DPA language","Training LMS for annual AI literacy","Special education and HR process owners"],"constraints":["Staff-only scope (no student-use controls in this policy)","Voluntary SEA guidance—local adoption varies","Yellow uses require enhanced oversight capacity"],"security":"Enforce FERPA/COPPA/CIPA/IDEA/HIPAA/DC privacy statute compliance, encryption, MFA, incident reporting, and prohibition of consumer-grade AI for PII.","proofOfValue":["Attempt Red workflows and show hard blocks","Yellow grading draft requires human accept before gradebook write","Audit log of blocked consumer AI PII attempts","Training completion gate before first AI login"]},"delivery":{"work":"Localize stoplight language, configure allowlist, deliver pre-use/annual training, communicate to staff unions and SPED leads, and schedule yearly policy reassessment.","dependencies":["Legal review","SPED leadership","HR/eval owners","Identity and device management"],"ownership":"LEA CIO and chief academic officer jointly; principals enforce classroom staff practice.","skillsAdoption":"Mandatory AI literacy and data-protection training before access; annual renewal with proof of completion.","governanceCheckpoints":["Board or superintendent adoption of localized policy","Allowlist published","Quarterly Red/Yellow incident review","Annual policy refresh"],"acceptanceCriteria":["Documented Red prohibitions implemented in production tools","100% of AI-enabled staff accounts training-complete before enablement","No consumer GenAI approved for PII workflows","Yellow IEP/grading uses show human sign-off samples"],"risks":["Staff continue shadow consumer AI despite policy","Yellow grading/IEP drafting drifts into de facto automated decisions","Student-use gap leaves instructional AI unmanaged"]}},"retrievedAt":"2026-09-19T03:09:20Z","enrichedAt":"2026-09-19T03:13:15Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Annual AI literacy and data-protection training is a workforce prerequisite; SPED staff need clear Yellow vs Red boundaries so assistive drafting does not become eligibility automation.","procurementImplications":"RFPs should require enterprise-grade privacy/security, disablement of Red AI features, human-in-the-loop for Yellow grading/IEP drafting, and documented training-data/bias controls.","operatingModelImplications":"Staff AI governance becomes an operating rhythm: allowlist, training gates, yearly reassessment—not a one-time innovation pilot.","sourceVerification":{"openedUrl":"https://osse.dc.gov/sites/default/files/dc/sites/osse/page_content/attachments/LEA%20AI%20Model%20Policy%20Booklet%20Sept%202026.pdf","referenceExcerpt":"No AI tools may be used for this purpose. Unauthorized AI features of existing enterprise tools are prohibited.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}