{"resourceId":"osse-dc-lea-ai-survey-brief-may2026","versions":[{"version":"external-48f280f625e7ba2d59184e70d126d9fae9d5cdb8574ee9c3c02338c39a20b9ca","resource":{"id":"osse-dc-lea-ai-survey-brief-may2026","title":"OSSE/REL Mathematica LEA survey: only 45% of DC LEAs had staff AI policies; model guidance ranked top need","organization":"OSSE with Regional Education Laboratory at Mathematica","sector":"K-12 state education agency survey research","geography":"Washington, D.C., United States","publishedAt":"May 2026 (survey brief); fielded February 2026","publicationDate":"2026-05-01","eventDate":"2026-02-01","sourceName":"The State of AI in District of Columbia LEAs (survey results brief)","sourceLabel":"SEA/REL survey brief of LEA leaders","sourceUrl":"https://osse.dc.gov/sites/default/files/dc/sites/osse/page_content/attachments/AI%20Survey%20Results%20Brief%202026.pdf","evidenceClass":"government-evaluation","outcomeClass":"mixed","topics":["governance-procurement","data-security","accessibility-workforce","operating-model"],"finding":"In February 2026, OSSE and REL Mathematica surveyed DC’s 69 LEAs (59 responses collapsed to 39 unique LEAs). Only 45% reported written staff AI guidance and 34% reported student-use guidance. Procurement maturity was uneven (formal review 41%, informal/ad hoc 26%, not procuring 28%, unsure 5%). LEAs ranked model instructional AI policy as the most useful OSSE support, ahead of privacy guidance and educator training. Top adoption challenges included data privacy/security, tool quality, lack of federal/state guidance, bias, staff readiness, and uncertain instructional value. Why now: OSSE’s Sept 1, 2026 staff model policy was built explicitly in response to this gap and was not yet in the SLED k12 archive.","sledRelevance":"Quantifies the policy-readiness gap that SEAs and large districts must close before scaling staff AI—and explains demand for model policies over procurement templates alone.","evidence":"Opened the May 2026 PDF brief: policy prevalence (45%/34%), procurement pie chart categories, ranked desired supports, use-case interest bars, and challenge severity distributions. Cross-checked with OSSE press release citing the same 45% staff-policy figure as the impetus for the September model policy.","architectureImplications":"Many LEAs still lack formal AI procurement review—expect ad hoc tool sprawl until SEA templates and allowlists land; privacy and bias concerns should drive security review gates before instructional rollout.","governanceImplications":"Staff policy and student policy are separate adoption problems; SEAs should sequence model staff guidance first where LEAs request it most, then student-use and procurement templates.","securityPrivacyImplications":"Data privacy/security was among the most cited significant challenges—supports prioritizing DPAs and enterprise controls before expanding student-facing GenAI.","caveats":"Self-report LEA-leader survey (39 LEAs), not classroom outcome measurement; published May 2026 (pre-policy) so pair with the September model policy for current operational meaning. Interest bars are not evidence of effective use.","streamIds":["k12"],"roles":{"sales":"Sales framing: the buying trigger is incomplete local policy, not feature hunger. Open with how you help an LEA move from ad hoc review to written staff guidance, privacy language, and training—metrics buyers already know (45% staff / 34% student policy). Do not pitch personalized learning gains this survey does not measure; sell readiness scaffolding that matches the #1 requested SEA support.","engineering":"Engineering framing: assume many LEAs still have informal procurement. Deliver opinionated defaults—policy templates as config, privacy review checklists, and training gates—rather than open-ended tool catalogs. Instrument adoption so leaders can report staff-policy coverage and formal review status, the metrics this survey made salient.","delivery":"Delivery framing: start with a readiness baseline mirroring the OSSE items (staff policy y/n, student policy y/n, procurement maturity, top challenges). Then localize a staff model policy and schedule PD. Success is documented policy coverage and a formal review path, not hours of chatbot use."},"roleDetails":{"sales":{"customerProblem":"Most LEAs still lack written staff/student AI policies and formal AI procurement review while facing privacy and quality concerns.","stakeholders":["SEA program leads","LEA executives","CTOs","Procurement","Board members"],"discoveryQuestions":["Do you have written staff and student AI policies today?","Is AI procurement formal, ad hoc, or not happening?","Which challenge is prohibitive: privacy, quality, bias, or guidance gaps?","What SEA supports would unblock you first?"],"valueHypothesis":"A bounded policy-and-procurement readiness engagement converts survey-identified gaps into adoptible local controls before instructional scale-up.","potentialEngagement":"3-week LEA AI readiness baseline and staff-policy draft aligned to OSSE survey constructs.","unsupportedClaims":["That policy adoption raises achievement","That 45%/34% figures generalize outside DC","Guaranteed reduction in privacy incidents after templates"]},"engineering":{"fit":"Fit as a governance instrumentation and template layer; not a student tutoring product.","architectureIntegration":"Policy status fields in ITSM/inventory; procurement workflow states; training completion linked to IdP entitlements.","prerequisites":["LEA inventory of AI-touched tools","Executive sponsor for policy","Privacy officer involvement"],"constraints":["Survey is DC-specific","Self-report bias","No student outcome measures"],"security":"Prioritize privacy/security review gates because leaders already flag them as major challenges.","proofOfValue":["Baseline report matching OSSE metrics","Draft staff policy approved by counsel","Procurement checklist used on one live purchase"]},"delivery":{"work":"Run readiness survey, draft staff policy, stand up formal review steps, and plan educator PD against top challenges.","dependencies":["OSSE/SEA templates","Legal","PD providers"],"ownership":"LEA chief of staff or CIO owns readiness metrics; academics own instructional examples later.","skillsAdoption":"Train leaders to interpret readiness metrics and staff to follow new review steps.","governanceCheckpoints":["Baseline published","Staff policy adopted","Procurement SOP live"],"acceptanceCriteria":["Written staff AI guidance adopted","Formal AI review path documented","Top two challenges have named owners"],"risks":["Policy theater without enforcement","Skipping student-use policy after staff-only win","Overclaiming instructional value the survey does not support"]}},"retrievedAt":"2026-09-19T03:09:25Z","enrichedAt":"2026-09-19T03:13:15Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Staff readiness and training ranked as needed supports; interest in disability and EL use cases exists but is not evidence those uses are effective yet.","procurementImplications":"With only ~41% reporting formal AI review, vendors should expect uneven diligence and help buyers stand up review—not exploit ad hoc paths.","operatingModelImplications":"SEA support sequencing (model policy → privacy → PD) mirrors how LEAs ranked needs; operating plans should follow that order.","sourceVerification":{"openedUrl":"https://osse.dc.gov/sites/default/files/dc/sites/osse/page_content/attachments/AI%20Survey%20Results%20Brief%202026.pdf","referenceExcerpt":"45% of LEAs reported that they had written guidance or policy for school staff use.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}