From the K–12 edition of October 13, 2026
Fairplay Student-First AI Policy: independent learning-gain evidence, privacy/non-addictive design, and three-month phase-out for unapproved student GenAI
Fairplay (with Parabola Center for Law and Policy; Critical Internet Studies Institute) · Children’s advocacy / model K-12 board policy · United States (model for local school districts nationwide)
- Publisher
- Fairplay
- Original publication
- October 7, 2026 (press); model PDF retrieved 2026-10-13
- Source retrieved
- 2026-10-14
- Event date
- 2026-10-07
What happened
Fairplay’s Student-First AI Policy (model PDF accompanying an Oct 7, 2026 release) forbids student use of generative AI products unless independent evidence shows the specific product improves learning outcomes better than existing methods, protects privacy without addictive design, and does not perpetuate discrimination or inequities. Unapproved student-facing products and features are phased out within three months of adoption; exceptions cover legally mandated accommodations (IEP/504/ADA/health-provider necessity) and time-limited curriculum-necessary uses (e.g., AI coding with no equivalent). Any student, administrator, educator, or parent/caregiver may petition for consideration. Independent research must be peer-reviewed and free of vendor funding/material involvement. Educators may use approved tools for differentiation/instructional planning with review and disclosure, but not for grading or behavior monitoring. Independent digital-literacy instruction is required. Why now: not archived; pairs with today’s NY policy-vacuum census as a concrete high-bar template boards can adapt after moratoriums in NYC/LA.
Why it matters
Supplies districts with adaptable procurement and approval criteria that convert “responsible use” slogans into evidence, privacy, and equity gates—especially salient where board policies are still missing.
Evidence and measured results
Primary model policy text with defined terms (Addictive Design, Independent Auditor/Research), applicability, exceptions, petition process, and staff-use limits. Not an empirical impact evaluation; advocacy-authored; local counsel must adapt.
Limitations and uncertainty
Model policy, not enacted law. High ESSA “strong evidence” bar may exclude many tools. Authors are advocacy/legal scholars; consult local counsel. Does not measure learning outcomes itself.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-13; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Boards lack a ready high-bar framework to approve or remove student GenAI after bans/moratoriums and amid vendor claims.
- Stakeholders
- School board
- Superintendent
- Privacy counsel
- Special education
- Teachers union/association
- Parent advocates
- Students
- Discovery questions
- Which student GenAI tools are live today?
- Can GenAI features be disabled in current SaaS?
- What evidence standard will the board accept?
- How are IEP/504 AI accommodations handled?
- Value hypothesis
- Localizing this model can give boards an enforceable approval path that parents and educators can petition into, reducing ad-hoc vendor rollouts.
- Potential engagement
- 6–10 week policy localization: inventory, draft adaptations, exception procedures, Approved List standing process, phase-out plan.
- Unsupported claims
- That the model is already law
- Guaranteed learning gains from any approved tool
- That every vendor can meet ESSA strong-evidence immediately
Pre-sales engineering
Role takeaway
- Fit
- Policy-driven student GenAI allowlist with feature-level disable and contractual change control.
- Architecture and integration
- Approved List service; feature flags; contract metadata for GenAI clauses; workflows for data destruction certificates at phase-out.
- Prerequisites
- Full inventory of GenAI features
- Legal review of contracts
- Identity groups for staff vs student use
- Petition/ticket workflow
- Constraints
- Three-month phase-out clock after adoption
- ESSA strong-evidence bar
- Curriculum exceptions expire end of year
- Security
- Enforce no-ad / no-biometric / no-addictive-design certifications; minimize student data processing; require deletion proof on removal.
- Proof of value
- Percent of student GenAI tools on Approved List or disabled
- Phase-out milestones met
- Exception register completeness
Delivery
Role takeaway
- Work
- Facilitate policy adoption, inventory and disablement, petition hearings, literacy curriculum from independent providers, and annual exception renewals.
- Dependencies
- Board sponsorship
- Special education for accommodations
- Procurement for contract amendments
- Independent literacy partners
- Ownership
- Board/tech subcommittee owns approvals; CIO owns disablement; academics own literacy; SPED owns accommodation exceptions.
- Skills and adoption
- Train staff on planning-only AI uses with mandatory review/disclosure; ban grading/behavior-monitoring AI uses.
- Governance checkpoints
- Adoption vote
- 30/60/90-day phase-out checks
- End-of-year exception expiry review
- Acceptance criteria
- Published approval criteria
- Unapproved student GenAI removed or disabled
- Documented accommodation/curriculum exceptions
- Risks
- Over-broad bans blocking assistive tech if exceptions fail
- Vendor capture of “independent” research
- Petition backlog
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?
Requires an Approved List, ability to disable GenAI features in existing products, contract clauses blocking silent GenAI feature adds, and data-deletion/return workflows within the three-month phase-out window.
Governance
Who approves, reviews and stays accountable for outcomes?
Move student GenAI from vendor marketing to petitionable, independently evidenced approvals with public process. Keep human ownership of grading and behavior monitoring.
Security and privacy
What data, permissions and controls need testing?
Mandates vendor certifications against ads/ad-business models, addictive design, and biometric collection, plus contractual privacy guarantees—raising the bar above generic DPAs.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Explicit exceptions for IEP/504/ADA and clinician-necessary uses preserve assistive pathways while default student GenAI stays gated; staff digital-literacy mandate includes cognitive and social-emotional risk topics.
Procurement
What should contracts, pricing and exit terms secure?
Contracts must bar silent GenAI feature adds, require privacy/non-addictive certifications, and support termination/data-return when products fail approval.
Operating model
Which teams own the service once it runs?
Community petition + subcommittee review becomes the steady-state intake for student GenAI, replacing vendor-led classroom pilots.
Publication history
- 2026-10-13K–12 · Issue 373 resources
Stable resource ID: fairplay-student-first-ai-policy-20261007