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 September 7, 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.
A note from the founder
Why Every New Tenet Gateway Customer Gets a Parent Chatbot This Semester
Every new Tenet Gateway customer gets a parent-facing AI chatbot included this semester. It answers ordinary family questions from approved public district information and cites the district pages behind each response.
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 15 states in detail, including Virginia's 2026 guidance-and-policy statute and Florida's proposed parent opt-in rule, alongside Oklahoma's opt-out and Maryland's 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.
Florida AI rule
Florida's Proposed K-12 AI Rule (6A-1.0957): District Requirements and the July 1, 2027 Deadline
The Florida State Board of Education is scheduled to vote on September 16, 2026 on an amendment to its Internet Safety Policy rule that would require every district school board and charter governing board to adopt and implement an AI instructional-tools policy by July 1, 2027. The proposed text uses parent opt-in, not opt-out, and does not contain a January 1, 2027 date.
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.
Virginia AI policy
Virginia HB 1186 and SB 394: What School Boards Must Do Under Code Section 22.1-20.2:1
Effective July 1, 2026, Virginia Code section 22.1-20.2:1 directs the Department of Education to compile current uses of AI in instruction and publish guidance, and requires every local school board to establish, implement, and enforce policies consistent with that guidance. The law sets no calendar deadline for either step and does not contain the chatbot provision reported during the session.
Approval reference
AI Product Age and Consent Requirements for K-12: A Dated Table (2026)
Consumer AI products set their own age floors, and a school signing up students does not change them. This table records, from each vendor's terms as of September 7, 2026, which products admit students, at what age, who must consent, and whether student data is used for training, so a tool-approval decision starts from the vendor's rule rather than a guess.
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.
Chromebook administration
Blocking and Allowing AI on School Chromebooks: The Complete Admin Console Map (2026)
A district cannot block AI on Chromebooks with one setting, because AI now lives in at least twelve places: the Gemini app, Gemini inside Docs and Gmail, the Classroom Gemini tab, Google Search AI Overviews and AI Mode, Gemini in the Chrome browser, Chrome and ChromeOS built-in features, Web Store extensions, third-party chatbot sites, Android apps, and guest or personal sessions. This map gives the control for each, verified against Google's own documentation on September 7, 2026, and what each control leaves open.
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.
Federal privacy
COPPA and AI Chatbots in Elementary and Middle School: When the School Can Consent
A school can stand in for a parent under COPPA, but only inside narrow limits: the collection must be for the use and benefit of the school and no other commercial purpose, the vendor must give the school direct notice and a review-and-delete path, and the vendor cannot push COPPA compliance onto the district. Most consumer AI chatbots fail that test before the question of consent is even reached.
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.
Texas AI policy
Texas School AI Rules Without TEA Guidance: HB 3512, TRAIGA, SB 1964, and What Districts Should Adopt
Texas has no education-agency AI guidance, so districts assume they have no obligations. They have four: AI awareness training under HB 3512, the state AI code of ethics and heightened-scrutiny review under SB 1964 and the DIR rule effective March 18, 2026, disclosure and prohibited-practice duties under TRAIGA effective January 1, 2026, and the existing student online personal information protections in the Education Code.
Instructional policy
Can Teachers Use AI to Grade? District Policy Language and What Utah and DC Actually Require
Reporting has flattened this question into a ban. The real picture is a tiered one: no authority found allows AI to set a final grade, most allow AI to draft formative feedback with review, and the fights are over the middle, where AI suggests a score a teacher accepts. Utah made it binding in 2026, DC published the most detailed model tiers, and both require disclosure.
Special education
AI in IEP and 504 Writing: What IDEA Requires, What May Enter a Prompt, and What Districts Should Adopt
A majority of special education teachers reported using AI to help develop an IEP or 504 plan in 2024-2025, and about one in five had received any training on the risks. There is no federal guidance on the practice, and the one on-point federal document has been rescinded. This guide works from the underlying regulations instead: what makes a plan legally sufficient, and what a prompt may contain.
Meeting records
AI Notetakers in IEP, 504, and Staff Meetings: Consent, Records, and Vendor Rules
Federal law neither authorizes nor prohibits recording an IEP meeting, but a district policy on recording must allow exceptions where recording is necessary for a parent to understand the proceedings, and any recording the district maintains is an education record. AI notetakers add three problems on top: platform defaults that change without a district decision, all-party consent laws in about a dozen states, and vendors that joined the meeting without a data agreement.
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.
District implementation guide
How to Pilot an AI Tool Before a District-Wide Rollout
A useful AI pilot tests one defined use, configuration, user group, and data boundary. This playbook helps school districts set success and stop criteria, test with synthetic data, document evidence, and decide what may move forward.
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.