NIST, U.S. Department of Education, FTC, and other original authorities where applicable.
Tenet resource library
K-12 AI governance guides, product comparisons, and evaluation tools.
Source-backed comparisons, learning-evaluation frameworks, current state-law tracking, and implementation tools for district leaders turning AI principles into operational decisions.
Browse the libraryEvery resource shows when it was written and last checked.
Clear definitions, decision frameworks, and downloadable working templates.
Dated answers for changing requirements and product coverage.
These references state what was reviewed, when it was reviewed, and where the underlying evidence comes from.
Supported-product matrixTenet Supported AI Products: Capability Matrix (August 2026)
A dated matrix of Tenet Edge support for ChatGPT, Claude, Gemini, Copilot, Grok, MagicSchool, SchoolAI, and Brisk Boost, plus unapproved AI blocking boundaries.
Reviewed August 11, 2026 →State policy trackerState K-12 AI Laws and Guidance (2026)A dated, source-linked comparison of verified state K-12 AI laws and education-agency guidance, with clear labels for binding duties and recommendations.
Reviewed August 17, 2026 →Do not collapse product quality, teaching quality, and learning into one score.
Use a common methodology to compare exact products and accounts, define the permitted level of AI assistance, and evaluate local-model privacy without mistaking infrastructure for a student-ready experience.
01How We Evaluate AI for K-12 Learning
A useful K-12 AI scorecard must identify the exact product and model tested, reproduce realistic school tasks, separate vendor documentation from observed behavior, and never confuse a convincing tutoring response with evidence that students learned.
18 min read →02K-12 AI Assistance Ladder for Teaching and LearningUse the lowest level of AI assistance that preserves the learning objective and the evidence of mastery an educator needs. Move higher only when additional assistance is part of the objective, removes an access barrier without replacing the target skill, or supports staff work under appropriate review.
15 min read →03ChatGPT for Teachers vs Claude for Teachers vs Gemini for EducationChatGPT for Teachers, Claude for Teachers, and Gemini for Education are not interchangeable. Their eligible users, district controls, instructional context, student access, integrations, and current deployment models differ in ways a school district should evaluate before approval.
15 min read →04Local AI in K-12 Schools: Uses, Benefits, Risks, GuardrailsLocal AI can keep approved inference within district-controlled infrastructure and preserve an evaluated model release. It also makes the district responsible for hardware, security, safeguards, evaluation, and operations.
20 min read →The complete library
Build the district governance operating model.
Start with architecture and current state requirements, then move into tool approval, data boundaries, readiness, accommodations, and a working AI application register.
District AI architecture
The Two Planes of District AI: Direct Use at the Edge and AI in the Backend
Direct AI use and backend application AI create different enforcement problems. A shared control plane keeps identity, purpose, data boundaries, model eligibility, and audit rules aligned across both.
Definitive guide
AI Governance in K-12: A Practical District Guide
K-12 AI governance is the operating system a district uses to decide which AI uses are allowed, under what conditions, for whom, with which data, and with what evidence and review.
Architecture explainer
AI Gateway vs. AI Governance Control Plane for School Districts
Traffic management and governance are complementary. Districts need infrastructure that moves model requests and a policy layer that decides which requests are authorized.
Downloadable template
AI Tool Vetting and Approval Template for K-12 Schools
Approve a defined educational use and deployment, not a vendor name. This template gives district teams a repeatable record for evidence, conditions, ownership, and review.
Student data protection
Why Data Loss Prevention Matters in K-12 AI
A data privacy agreement governs the provider. A no-training commitment limits one use of customer content. Neither control decides whether a student record was necessary or authorized in a particular AI request.
Supported-product matrix
Tenet Supported AI Products: Capability Matrix (August 2026)
Tenet Edge provides deep governed support for eight named AI product experiences on district-managed Chrome. A separate optional layer can block detected unapproved AI chat and writing interfaces. This matrix separates those capabilities and their rollout boundaries.
Federal student privacy
FERPA and AI in K-12 Schools: A District Guide
FERPA does not approve or prohibit AI products by category. A district must identify the education records involved, establish consent or a valid exception, satisfy every condition of that exception, and control how the provider uses and maintains the records.
Shadow AI governance
Shadow AI in K-12 Schools: Audit, Detect, Approve, or Block
Shadow AI is an AI tool or feature used outside the district's recorded review and approval process. Districts need a maintained inventory, more than one discovery source, a repeatable decision workflow, and technical controls that match the exact product surface.
Market map
K-12 AI Governance Software: What Counts, What Doesn't, and How Districts Should Evaluate It
K-12 AI governance software turns district policy into controls across AI products and district-built AI applications. It is different from a student chatbot, a web filter, or a software-vetting database.
District AI application strategy
School District AI Chatbots: Build vs. Buy Guide (2026)
Buying can launch one assistant quickly. Building can give a district more control and a reusable foundation for future AI applications. This guide explains the tradeoffs, costs, architecture, and safest first scope.
Maturity framework
K-12 AI Governance Readiness Assessment
A readiness assessment should reveal the next operating decisions a district needs to make. This eight-domain framework evaluates both direct AI use and backend application AI without producing a false compliance score.
State policy tracker
State K-12 AI Laws and Guidance (2026)
This primary-source tracker separates enacted district duties, state-level implementation duties, model policies, and nonbinding guidance. It now covers 13 states in detail, including Oklahoma's student-facing AI opt-out and Maryland's local policy and coordinator requirements.
Downloadable template
District AI Application Register Template
An AI application register turns hidden backend use into an accountable portfolio. Every use receives an owner, purpose, data boundary, deployment, decision, constraints, and lifecycle record.
Ohio AI policy
Ohio HB 96 School AI Policy Requirements After July 2026
Ohio school districts, community schools, and STEM schools were required to adopt an AI-use policy by July 1, 2026. The state model is available, but the statute says covered schools may adopt it rather than requiring its use.
Planning guide
AI, Accommodations, and Student Privacy in K-12
AI can support access only when districts preserve human decision-making, minimize plan-derived data, authorize the purpose, control each technical surface, validate the result, and protect student privacy.
Software evaluation
K-12 AI Governance Software Buyer's Guide and RFP Checklist
The right K-12 AI governance software should apply district decisions where AI is used, protect data before supported sends, preserve teacher and grade context, and give the district usable evidence without building a transcript warehouse.
Oklahoma school AI law
Oklahoma SB 1734 School AI Requirements and Parent Opt-Out
Oklahoma SB 1734 took effect July 1, 2026. It requires safeguards for student-facing AI, annual family disclosure, a parent opt-out without academic penalty, and a district board policy before the 2027-2028 school year.
Privacy and architecture
Data Boundaries for K-12 AI Applications
An AI data boundary is a documented authorization boundary. It says which identity may use which data for which purpose through which model deployment, under which constraints and review.
Maryland school AI law
Maryland AI Ready Schools Act: SB 720 District Requirements
Maryland SB 720 became Chapter 634 and took effect June 1, 2026. Local school systems must designate an AI coordinator and implement a policy aligned with state guidance within 120 days after that guidance is released.
Reference
K-12 AI Governance Glossary
A stable vocabulary helps district leaders, educators, technologists, privacy teams, vendors, and families make the same AI decision with the same meaning.
California school AI policy
California School AI Model Policy: What SB 1288 Means for Districts
California SB 1288 required state AI guidance and a model policy. CDE now publishes that model, but expressly labels it exemplary and nonmandatory. Districts should use it as a reviewed starting point, not misstate every recommendation as law.
Policy implementation
K-12 AI Acceptable Use Policy Implementation Checklist
A useful AI acceptable use policy tells students and staff what is allowed, under which conditions, with which accounts and data, and what happens when the rules are unclear or violated. Implementation requires owners, examples, training, technical alignment, reporting, and scheduled review.
Governance template
District AI Governance Committee Charter Template
An AI governance committee needs more than a roster and a meeting calendar. Its charter should define which decisions it owns, which evidence it requires, who is accountable, how urgent issues move, and how the district reviews its work.
Procurement and privacy
AI Vendor and DPA Review Questions for School Districts
An AI vendor review should bind the exact product, account, purpose, data, model deployment, and enabled features to enforceable terms. A generic security packet or vendor-level DPA cannot answer every question about a specific district use.
Incident readiness
K-12 AI Incident Response Playbook for School Districts
An AI incident response plan should connect the district's existing privacy, cybersecurity, student safety, civil rights, academic, vendor, and communications procedures. It adds AI-specific triage for model behavior, data boundaries, retrieval, account configuration, and automated actions.
Student AI governance
How School Districts Can Govern Student Use of ChatGPT, Gemini, Claude, and Other AI Tools
School districts can govern student use of ChatGPT, Gemini, Claude, and other AI tools by combining four controls: approved access, policy inside supported AI interactions, data loss prevention, and rules that reflect the student's grade and classroom context.
Local AI district guide
Local AI in K-12 Schools: Uses, Benefits, Risks, Guardrails
Local AI can keep approved inference within district-controlled infrastructure and preserve an evaluated model release. It also makes the district responsible for hardware, security, safeguards, evaluation, and operations.
Evaluation methodology
How We Evaluate AI for K-12 Learning
A useful K-12 AI scorecard must identify the exact product and model tested, reproduce realistic school tasks, separate vendor documentation from observed behavior, and never confuse a convincing tutoring response with evidence that students learned.
Instructional framework
K-12 AI Assistance Ladder for Teaching and Learning
Use the lowest level of AI assistance that preserves the learning objective and the evidence of mastery an educator needs. Move higher only when additional assistance is part of the objective, removes an access barrier without replacing the target skill, or supports staff work under appropriate review.
K-12 product comparison
ChatGPT for Teachers vs Claude for Teachers vs Gemini for Education
ChatGPT for Teachers, Claude for Teachers, and Gemini for Education are not interchangeable. Their eligible users, district controls, instructional context, student access, integrations, and current deployment models differ in ways a school district should evaluate before approval.
Local model profile
Meta Muse Glimmer for Schools: Privacy and Guardrails
Muse Glimmer can reduce third-party disclosure by keeping inference on district-controlled hardware. The downloadable model is not, by itself, a student-ready product, privacy program, or compliance determination.
Working templates
Put governance into a format people can maintain.
Download clean CSV starting points for an AI application register and tool-vetting workflow. Adapt the fields to your district's owners, legal process, instructional review, and records practices.
Turn research into action
Use the resources. Then test the operating model.
A bounded Tenet pilot can validate how your district's policy, tools, data boundaries, and technical environment fit together.