AI governance in K–12 education
AI Governance in K–12 Education
Schools are giving students access to generative AI systems that can teach, guide, steer, and influence a child's thinking in real time. But many districts are still governing these systems as ordinary digital tools.
That mismatch is the accountability gap this framework addresses.
When a teacher interacts with a child, there is a license, supervisor, employer, and risk structure behind that interaction. When a generative AI system performs similar functions on a school-issued device or district account, those structures may not exist or may not have been evaluated against this type of system.
Generative AI is not just another classroom tool. It can generate novel content, hold open-ended conversations, retain context across time and sessions, operate across supervised and unsupervised settings, and change through vendor or model updates the school may not control or detect.
We believe these systems warrant a new governance classification — one that requires answers to questions that existing frameworks were not designed to ask. What systems are accessible? Who is responsible for outputs? Can interactions be reconstructed? Can families opt out without educational penalty? Are harms covered by insurance or risk financing? Who controls changes to the system over time?
That is what the Digital Childhood Council exists to define.
The insurance signal
In 2026, insurers began introducing endorsements addressing generative AI outputs, including ISO endorsements CG 40 47 and CG 40 48. Their existence is a signal worth treating seriously: it suggests insurers see AI-generated outputs as a distinct exposure, separate from how standard coverage was written.
This raises a forward-looking governance question: has student use of generative AI on school-issued devices been explicitly evaluated and affirmatively covered within current underwriting structures?
Frameworks
Three interconnected policy frameworks
Each framework addresses a distinct layer of the accountability problem.
The IAF is the primary instrument for risk managers, legal counsel, underwriters, and school administrators.
Primary framework · Risk & insurance
Institutional Accountability Framework (IAF)
Schools are deploying generative AI functioning as an instructional actor while governing it as a digital tool. That gap is where governance breaks down.
The IAF defines the conditions under which schools remain structurally accountable when AI systems interact with students in real time. Generative AI systems may lack an internal mechanism that binds content to institutional authority before a student receives it. The IAF identifies what this framework treats as required for a district's governance of that deployment: supervision that is enforceable, interactions that can be attributed and reconstructed, and a named entity that can intervene, investigate, and be held accountable when harm occurs. Vendor disclaimers define what the vendor has not accepted. The IAF identifies what the district still holds.
Risk managers Legal counsel Underwriters School boards District administrators
Structural framework
Structural Non-Attribution Risk Framework (SNAR)
Examines how generative systems operate when they lack reliable harm signals, clear attribution, consequence binding, and constraint mechanisms — and why failures propagate at scale.
Legislative infrastructure
Digital Signal Infrastructure Act (DSIA)
Establishes OS-level Micro J-Tag signals for age and jurisdiction observability across digital ecosystems — infrastructure that makes enforcement of child-protection laws possible.