{"resourceId":"montana-ai-k12-guidelines-v2-202608","versions":[{"version":"external-e8fed93b6ebdb719171224e4df6308af006964c6d7f0bc9aab41fed1b565857c","resource":{"id":"montana-ai-k12-guidelines-v2-202608","title":"Montana OPI AI Guidelines v2: preserve productive struggle; assisted performance ≠ mastery; companion AI is not support","organization":"Montana Office of Public Instruction / Montana Digital Academy Frontier Learning Lab","sector":"State education agency guidance","geography":"United States — Montana (advisory for local districts; transferable principles)","publishedAt":"August 2026 (Version 2.0)","publicationDate":"2026-08-01","eventDate":null,"sourceName":"Montana Office of Public Instruction","sourceLabel":"Montana AI in K-12 Education Guidelines v2","sourceUrl":"https://opifiles.mt.gov/Portals/182/Page%20Files/Homepage/Docs/Montana%20Artificial%20Intelligence%20in%20K12%20Education%20Guidelines.pdf?ver=2025-10-14-134920-167","evidenceClass":"standards-guidance","outcomeClass":"emerging","topics":["governance-procurement","knowledge-work","data-security","accessibility-workforce"],"finding":"Montana’s August 2026 Version 2.0 guidelines (advisory) tell districts to adopt evidence-driven AI use, preserve productive struggle, keep humans in the loop, and protect student data. They warn that better assisted task performance does not prove independent mastery, retention, or transfer; urge a clear continuum of AI not-permitted / bounded / expected per activity; and instruct districts not to treat AI as counseling or crisis support, with separate review for companion-style tools. Immediate actions: cross-functional AI team, PD before student use, community communication; vendor checklist covers FERPA/COPPA, bias, engagement dark patterns, accessibility, and privacy agreements.","sledRelevance":"Why now: supplies independent SEA language matching this edition’s engagement-vs-mastery and companion/supervision themes for districts writing first policies.","evidence":"Official state guidance PDF opened in full; advisory, not statute; twice-annual revision process disclosed; AI assisted drafting with human responsibility noted.","architectureImplications":"Build permissioned classroom AI modes, approved-tool registry, minimized logging, and companion-AI quarantine paths.","governanceImplications":"Local boards retain authority; use checklists and twice-yearly review; consult tribal sovereignty where cultural data/content implicated.","securityPrivacyImplications":"Mandatory-style vetting expectations for privacy/security/consent; deepfake protocols with law-enforcement escalation; no AI-as-counselor.","caveats":"August dated—older than last run but not previously archived; Montana-specific legal anchors; rural capacity assumptions may differ for large urban LEAs.","streamIds":["k12"],"roles":{"sales":"Sales framing: Montana’s Aug 2026 AI Guidelines v2 give SEA-level language buyers can cite—evidence-driven adoption, preserving productive struggle, and refusing AI as a counseling substitute. Why now: it complements this week’s Florida district opt-in/supervision work and Hechinger’s access-without-engagement finding by separating assisted task performance from independent mastery. Advisory guidance, not a mandate—position as a policy accelerator for rural and local-control districts adapting checklists to board authority.","engineering":"Engineering framing: implement a continuum of student AI permissions per activity; keep humans reviewing generative outputs; add companion-AI separate review for relationship-building designs; require DPA or Montana Student Data Privacy Agreement equivalents. Accessibility and UDL evidence belong in vendor packets, and engagement dark patterns should be disclosed and disableable. Interpretation of advisory state guidelines for transferable architecture patterns.","delivery":"Delivery framing: start with Montana’s three immediate actions—local AI team, evidence-informed PD before student use, and community communication—then walk the roadmap from vision through policy, capacity, access, and twice-yearly review. Keep tribal consultation and rural shared-service options in scope where relevant. Interpretation for SLED delivery planning; local boards retain final policy authority."},"roleDetails":{"sales":{"customerProblem":"Districts need usable AI policy language that distinguishes learning assistance from mastery, governs companion chatbots, and fits local-control/rural capacity constraints.","stakeholders":["superintendent","school board","curriculum leaders","IT/security","special education/504 leads","tribal education liaisons where applicable"],"discoveryQuestions":["Do local policies define when AI is banned, bounded, or expected for an assignment?","How do you evaluate whether students can show learning without the tool?","Are companion-style chatbots reviewed on a stricter track than prep tools?","Is there a cross-functional AI guidance team beyond IT?"],"valueHypothesis":"Adopting Montana-style principles and vetting checklists shortens policy cycles while aligning procurement to productive-struggle and privacy expectations.","potentialEngagement":"Facilitated policy gap analysis mapping Montana v2 principles to local AUP, integrity, and procurement checklists, with a board briefing draft.","unsupportedClaims":["That Montana guidance is legally binding outside Montana","Guaranteed learning gains from following the checklist","That educator prep-AI time savings automatically improve student outcomes"]},"engineering":{"fit":"Strong fit for districts drafting first AI policies or refreshing AUPs amid companion-bot and mastery concerns.","architectureIntegration":"Approved-tool registry with purpose tags; classroom modes for AI-not-permitted / bounded / expected; logging minimized to purpose; synthetic-media incident playbooks.","prerequisites":["Cross-functional AI guidance team","Vendor privacy agreement template","Family communication channel for approved uses","Accessibility review capacity"],"constraints":["Advisory only; local board retains authority","Rural bandwidth and staffing limits shared-service options","Tribal data sovereignty consultation may be required for relevant content"],"security":"FERPA/COPPA-aware vetting; minimize PII in prompts; deepfake/CSAM escalation to law enforcement; no AI presented as crisis counseling.","proofOfValue":["Sample assignments show clear AI permission labels","Pilot evaluates unaided demonstration of learning","Companion-style tools either blocked or separately approved"]},"delivery":{"work":"Stand up AI guidance team, draft local permission continuum, run educator PD before student enablement, publish family guidance, execute vendor checklist on current tools.","dependencies":["board policy calendar","MTSBA or equivalent model policies","legal/accessibility counsel","PD time"],"ownership":"Superintendent designates team lead; board adopts policy; IT owns vetting records; principals own classroom permission clarity.","skillsAdoption":"PD on verification, productive struggle design, disclosure expectations, and when to escalate companion-AI concerns.","governanceCheckpoints":["Team charter approval","Pre-student-use PD completion","First vendor checklist cohort complete","Six-month guidance review"],"acceptanceCriteria":["Written continuum for AI not-permitted/bounded/expected","No companion AI presented as counseling","All student-facing AI tools have completed checklist or are blocked","Family communication on approved tools published"],"risks":["Policy copied without local capacity to enforce","Teachers skip verification of AI materials","Students use unmanaged companion bots outside approved lists"]}},"retrievedAt":"2026-09-27T22:08:52-05:00","enrichedAt":"2026-09-27T22:09:30-05:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"UDL/accessibility evidence required in vetting; educator PD before student enablement; recognize rural staffing limits.","procurementImplications":"Do not deploy without checklist completion and privacy agreement; demand disclosure of engagement-maximizing design features.","operatingModelImplications":"Sequence team → PD → family communication before student AI; monitor and revise twice yearly.","sourceVerification":{"openedUrl":"https://opifiles.mt.gov/Portals/182/Page%20Files/Homepage/Docs/Montana%20Artificial%20Intelligence%20in%20K12%20Education%20Guidelines.pdf?ver=2025-10-14-134920-167","referenceExcerpt":"Improvements in immediate task performance do not necessarily demonstrate independent mastery.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}