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A practical architecture for ownership, control, trusted knowledge, responsible use, and human review in K-12 AI environments.
Overview
K-12 organizations are moving from experimentation with general-purpose chatbots toward AI environments that can support institutional work. That shift requires a controlled architecture for identity, knowledge, applications, inference, human review, updates, and evaluation.
The paper presents a local-first architecture that can keep sensitive or routine work inside the district while still allowing selected cloud models when policy and the task make that appropriate.
Inside the resource
Why private AI is an architecture of identity, applications, knowledge, models, operations, and evaluation—not just a local model.
How local-first can coexist with selected external models under explicit organizational control.
Why curated knowledge, retrieval, source traceability, and versioning are first-class system capabilities.
How purpose-built applications reduce prompt burden and make human review, policy, and evaluation easier to design into the workflow.
How districts can move from experimentation toward pilots with defined governance and measurable expectations.
Engineering context
The architecture is intended to be portable across hardware tiers and model choices while preserving the district’s knowledge, workflows, and control plane.
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