{"resourceId":"msde-maryland-k12-ai-guidance-framework-20260224","versions":[{"version":"external-892fd45fe468c47b830603e17d0d41744e98bde7ccbd8f39c4c23bb299b2dcb2","resource":{"id":"msde-maryland-k12-ai-guidance-framework-20260224","title":"MSDE K–12 AI guidance sets human-centered statewide guardrails LEAs must operationalize under SB720 deadlines","organization":"Maryland State Department of Education (MSDE)","sector":"State education agency standards / guidance","geography":"Maryland, United States","publishedAt":"February 24, 2026 State Board information item (guidance last updated February 2026; LEA policy clock running through fall 2026)","publicationDate":"2026-02-24","eventDate":"2026-02-24","sourceName":"Artificial Intelligence Guidance for K-12 Schools (MSDE State Board packet)","sourceLabel":"Official SEA guidance framework PDF and companion planning tools","sourceUrl":"https://marylandpublicschools.org/stateboard/documents/2026/0224/artificial-intelligence-guidance-a.pdf","evidenceClass":"standards-guidance","outcomeClass":"emerging","topics":["governance-procurement","data-security","accessibility-workforce","operating-model","knowledge-work"],"finding":"MSDE’s February 2026 statewide AI framework centers human judgment, equity, privacy, academic integrity, professional learning, AI literacy, operational oversight, and procurement/vendor governance. It clarifies SEA vs LEA vs school roles, rejects AI-detection tools as a primary integrity strategy, requires human review of high-stakes operational AI, and supplies Local Action Planning Guide plus AI Tools Evaluation Rubric. Newly relevant on 2026-09-22 because Maryland LEAs face fall policy adoption deadlines under the AI Ready Schools Act (e.g., Frederick County cites Oct 22), and districts are colliding with suite-embedded GenAI before local policy is finished.","sledRelevance":"Primary template other states and vendors will be scored against in Maryland procurements: rubric-based vetting, human oversight, no detection-first integrity, and continuous vendor monitoring.","evidence":"Opened the MSDE State Board PDF end-to-end via WebFetch/pdftotext: purpose memo, eight critical elements, role definitions, privacy/equity/integrity/PL/literacy/ops/procurement sections, Local Action Planning Guide stages (Exploring through Sustaining), and explicit statement that MSDE does not approve specific tools.","architectureImplications":"LEA stacks need an AI tool registry tied to rubric scores, human-in-the-loop checkpoints for predictive/ops tools, and assessment redesign away from detector dependence.","governanceImplications":"Local boards must adopt aligned policy, designate AI coordinators (per statute context), and treat approval as ongoing—not one-time—vendor governance.","securityPrivacyImplications":"FERPA/COPPA/PPRA/IDEA/ADA/504/COMAR compliance, data minimization, contractual bans on advertising/profiling/unrelated training, and family transparency on retention/opt-outs.","caveats":"Guidance itself is not a measured-outcome study; February packet predates some fall 2026 enablement fights; full statutory text lived in SB720 companions not this PDF alone.","streamIds":["k12"],"roles":{"sales":"Sales framing: Maryland deals now run through MSDE’s evaluation rubric and human-oversight language. Bring a completed rubric packet, data-use attestations, and a plan for continuous monitoring—not a demo of chat novelty.","engineering":"Engineering framing: instrument bias/outcome monitoring hooks, human-review queues for any placement/early-warning features, and exportable model-change notices for LEA re-approval.","delivery":"Delivery framing: help the LEA finish Local Action Planning Guide stages, stand up cross-functional AI leadership, and sequence policy before classroom scale. Acceptance is an adopted local policy mapped to the eight critical elements."},"roleDetails":{"sales":{"customerProblem":"LEAs must adopt SB720-aligned AI policy and vet tools without a state-approved product list.","stakeholders":["MSDE-facing AI coordinator","superintendent cabinet","board","privacy counsel","equity office","procurement"],"discoveryQuestions":["Which Local Action Planning Guide stage is the LEA in for each critical element?","Do you already use MSDE’s AI Tools Evaluation Rubric?","Which high-stakes operational AI uses require documented human review?","How will you refresh approvals when vendors ship new GenAI features?"],"valueHypothesis":"Vendors that pre-fill MSDE rubric evidence and continuous-monitoring commitments shorten LEA political and legal risk under the fall deadline.","potentialEngagement":"3-week Maryland readiness workshop: rubric scoring, policy gap map, and board briefing deck tied to the eight critical elements.","unsupportedClaims":["That MSDE endorsement of a specific vendor exists","Guaranteed learning gains from adopting the framework","That detectors satisfy academic-integrity expectations"]},"engineering":{"fit":"Fit for LEAs building governed AI portfolios; poor fit for consumer tools lacking transparency disclosures.","architectureIntegration":"Tool registry plus rubric scores; contract metadata (training bans, retention); human-review workflow for ops AI; assessment platforms redesigned for process evidence.","prerequisites":["cross-functional AI team","legal review capacity","inventory of embedded AI features","family communication templates"],"constraints":["local autonomy on tool choice","rapid vendor feature changes","uneven rural/urban capacity"],"security":"Encryption, access control, breach processes, minimization, and bans on biometric/behavioral collection unless legally required and justified.","proofOfValue":["Completed rubric packet for one pilot tool","Documented human override path for an early-warning recommendation","Family notice template covering data retention and opt-out"]},"delivery":{"work":"Run readiness staging, draft local policy from MSDE considerations, train role-specific staff, and schedule continuous review.","dependencies":["board calendar","DoIT/procurement rules where applicable","special education and multilingual teams","CIO"],"ownership":"LEA AI coordinator with cabinet sponsorship; MSDE provides guidance only.","skillsAdoption":"Role-specific PL for teachers, admins, and specialists; AI literacy across grades as framed in guidance.","governanceCheckpoints":["Policy adoption","Rubric-gated pilots","Equity monitoring","Annual guidance refresh sync"],"acceptanceCriteria":["Local AI policy adopted and published","At least one tool fully scored on the rubric before classroom use","Human-review procedure documented for any predictive student system"],"risks":["Checkbox compliance without instructional redesign","Embedded suite AI bypassing registry","Over-reliance on detectors despite guidance"]}},"retrievedAt":"2026-09-23T03:11:05Z","enrichedAt":"2026-09-23T03:16:00Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Requires sustained educator PL and specialist involvement so AI does not replace IDEA/504 determinations or widen access gaps.","procurementImplications":"Structured evaluation criteria, contractual protections, audit rights, and ongoing monitoring replace one-time app-store approvals.","operatingModelImplications":"Shared SEA/LEA/school governance: state sets guardrails, LEA implements systems, schools operationalize norms—with continuous improvement.","sourceVerification":{"openedUrl":"https://marylandpublicschools.org/stateboard/documents/2026/0224/artificial-intelligence-guidance-a.pdf","referenceExcerpt":"Human judgment and student wellbeing remain central.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}