From the K–12 edition of September 22, 2026
MSDE K–12 AI guidance sets human-centered statewide guardrails LEAs must operationalize under SB720 deadlines
Maryland State Department of Education (MSDE) · State education agency standards / guidance · Maryland, United States
- Publisher
- Artificial Intelligence Guidance for K-12 Schools (MSDE State Board packet)
- Original publication
- February 24, 2026 State Board information item (guidance last updated February 2026; LEA policy clock running through fall 2026)
- Source retrieved
- 2026-09-23
- Event date
- 2026-02-24
What happened
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.
Why it matters
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 and measured results
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.
Limitations and uncertainty
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.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-09-23; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- 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
- Discovery questions
- 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?
- Value hypothesis
- Vendors that pre-fill MSDE rubric evidence and continuous-monitoring commitments shorten LEA political and legal risk under the fall deadline.
- Potential engagement
- 3-week Maryland readiness workshop: rubric scoring, policy gap map, and board briefing deck tied to the eight critical elements.
- Unsupported claims
- That MSDE endorsement of a specific vendor exists
- Guaranteed learning gains from adopting the framework
- That detectors satisfy academic-integrity expectations
Pre-sales engineering
Role takeaway
- Fit
- Fit for LEAs building governed AI portfolios; poor fit for consumer tools lacking transparency disclosures.
- Architecture and integration
- 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.
- Proof of value
- 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
Role takeaway
- 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.
- Skills and adoption
- Role-specific PL for teachers, admins, and specialists; AI literacy across grades as framed in guidance.
- Governance checkpoints
- Policy adoption
- Rubric-gated pilots
- Equity monitoring
- Annual guidance refresh sync
- Acceptance criteria
- 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
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
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.
Governance
Who approves, reviews and stays accountable for outcomes?
Local boards must adopt aligned policy, designate AI coordinators (per statute context), and treat approval as ongoing—not one-time—vendor governance.
Security and privacy
What data, permissions and controls need testing?
FERPA/COPPA/PPRA/IDEA/ADA/504/COMAR compliance, data minimization, contractual bans on advertising/profiling/unrelated training, and family transparency on retention/opt-outs.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Requires sustained educator PL and specialist involvement so AI does not replace IDEA/504 determinations or widen access gaps.
Procurement
What should contracts, pricing and exit terms secure?
Structured evaluation criteria, contractual protections, audit rights, and ongoing monitoring replace one-time app-store approvals.
Operating model
Which teams own the service once it runs?
Shared SEA/LEA/school governance: state sets guardrails, LEA implements systems, schools operationalize norms—with continuous improvement.
Publication history
- 2026-09-22K–12 · Issue 174 resources
Stable resource ID: msde-maryland-k12-ai-guidance-framework-20260224