From the K–12 edition of September 27, 2026
Montana OPI AI Guidelines v2: preserve productive struggle; assisted performance ≠ mastery; companion AI is not support
Montana Office of Public Instruction / Montana Digital Academy Frontier Learning Lab · State education agency guidance · United States — Montana (advisory for local districts; transferable principles)
- Publisher
- Montana Office of Public Instruction
- Original publication
- August 2026 (Version 2.0)
- Source retrieved
- 2026-09-28
What happened
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.
Why it matters
Why now: supplies independent SEA language matching this edition’s engagement-vs-mastery and companion/supervision themes for districts writing first policies.
Evidence and measured results
Official state guidance PDF opened in full; advisory, not statute; twice-annual revision process disclosed; AI assisted drafting with human responsibility noted.
Limitations and uncertainty
August dated—older than last run but not previously archived; Montana-specific legal anchors; rural capacity assumptions may differ for large urban LEAs.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-09-27; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- 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
- Discovery questions
- 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?
- Value hypothesis
- Adopting Montana-style principles and vetting checklists shortens policy cycles while aligning procurement to productive-struggle and privacy expectations.
- Potential engagement
- Facilitated policy gap analysis mapping Montana v2 principles to local AUP, integrity, and procurement checklists, with a board briefing draft.
- Unsupported claims
- 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
Pre-sales engineering
Role takeaway
- Fit
- Strong fit for districts drafting first AI policies or refreshing AUPs amid companion-bot and mastery concerns.
- Architecture and integration
- 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.
- Proof of value
- Sample assignments show clear AI permission labels
- Pilot evaluates unaided demonstration of learning
- Companion-style tools either blocked or separately approved
Delivery
Role takeaway
- 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.
- Skills and adoption
- PD on verification, productive struggle design, disclosure expectations, and when to escalate companion-AI concerns.
- Governance checkpoints
- Team charter approval
- Pre-student-use PD completion
- First vendor checklist cohort complete
- Six-month guidance review
- Acceptance criteria
- 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
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?
Build permissioned classroom AI modes, approved-tool registry, minimized logging, and companion-AI quarantine paths.
Governance
Who approves, reviews and stays accountable for outcomes?
Local boards retain authority; use checklists and twice-yearly review; consult tribal sovereignty where cultural data/content implicated.
Security and privacy
What data, permissions and controls need testing?
Mandatory-style vetting expectations for privacy/security/consent; deepfake protocols with law-enforcement escalation; no AI-as-counselor.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
UDL/accessibility evidence required in vetting; educator PD before student enablement; recognize rural staffing limits.
Procurement
What should contracts, pricing and exit terms secure?
Do not deploy without checklist completion and privacy agreement; demand disclosure of engagement-maximizing design features.
Operating model
Which teams own the service once it runs?
Sequence team → PD → family communication before student AI; monitor and revise twice yearly.
Publication history
- 2026-09-27K–12 · Issue 224 resources
Stable resource ID: montana-ai-k12-guidelines-v2-202608