DC Education Agency Releases AI Policy Guide for School Staff
The Office of the State Superintendent of Education (OSSE) released its first AI model policy for school staff on September 1. The customizable guidance is intended for local education agencies during the 2026 to 2027 school year. OSSE describes it as guidance only, not legal advice, and agencies adapt it rather than adopt it unchanged.
The policy uses a stoplight framework. It prohibits AI for high stakes decisions involving student discipline, teacher evaluations, physical surveillance, and eligibility for individual education programs or Section 504 accommodations. Limited use with safeguards may cover grading, drafting individual education program language, monitoring activity on agency devices, and supplemental educator coaching.
Staff may use AI with human review for lesson plans, customized student materials, tutoring plans, data analysis, school communications, and logistics. OSSE recommends approved enterprise tools, privacy compliance, staff training, annual renewal training, and continued human accountability.
OSSE said it produced the policy after a February 2026 survey of local agency leaders found that only 45% had established a staff AI policy, and that a model policy would be the most useful support the agency could provide.
The policy does not cover student AI use or tool procurement. OSSE also plans to offer two courses for educators on AI literacy, instructional decisions, and AI dilemmas.
We hope you enjoyed this article.
Consider subscribing to one of our newsletters like AI in Education, AI Policy Brief or Daily AI Brief.
Also, consider following us on social media:
More from: AI in Education
More from: Regulation
Subscribe to AI in Education
Weekly newsletter about AI in education. Covers AI-driven software for educators, schools, general innovations and regulatory updates.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read more