United States (federal) · guidance
OCR AI Guidance: what it requires, and the records to keep
OCR's November 2024 resource gave 21 examples of how school AI use could lead to investigations under Title VI, Title IX and Section 504, such as biased facial recognition, AI proctoring that flagged a disability, and unreviewed AI-written 504 plans. ED has since marked it formally rescinded, but the underlying statutes still apply, though Title VI enforcement now covers intentional discrimination only.
01 / What it requires
What OCR AI Guidance requires
Was addressed to K-12 schools and colleges receiving U.S. Department of Education funds.
- 01Underlying law: do not treat students differently by race, color, national origin, sex or disability through AI tools, directly or through vendors.
- 02Underlying law: respond to known harassment, including AI-generated harassment such as deepfakes, under Title VI, Title IX and Section 504.
- 03Underlying law: provide academic adjustments when AI proctoring or monitoring misreads disability-related behavior.
- 04Underlying law: have knowledgeable people review AI-drafted Section 504 plans and IEPs for each student's individual needs.
02 / AI work
Where it touches AI work
This was the first federal civil rights resource written specifically about AI in schools. Its 21 scenarios still show where AI use creates civil rights exposure, even though the document no longer states OCR policy.
03 / Dates
The dates that matter
Executive Order 14110 on AI, which prompted the resource
OCR releases the AI discrimination resource
Resource revised
EO 14110 revoked
ED removes disparate-impact liability from its Title VI regulations
04 / Enforcement
Who enforces it
None for the guidance itself. Title VI, Title IX, Section 504 and ADA Title II are enforced by ED OCR, with DOJ helping investigate complaints under a June 2026 agreement; the ultimate sanction is loss of federal funds.
05 / Records
Records an organisation keeps
What an auditor or an inquiry usually asks to see. Your obligations depend on your role and your facts.
- 01Vendor bias and accuracy reviews for AI tools affecting students
- 02Harassment complaint logs covering AI-generated content
- 03Accommodation requests and responses tied to AI proctoring
- 04Records of human review of AI-drafted 504 plans
Optional evidence · where Planisphere fits
Keep a record anyone can check
When your team meets a duty, Planisphere can seal the event it reports as a receipt.
A disclosure shown, a review signed, a model change approved: the event your system reports.
The event becomes a sealed receipt, dated and attributed to whoever is signed in.
Paste the receipt into Verify. No account or key.
A record supports your compliance evidence process. It is not a legal finding or a certification. Systems that call Planisphere's gate API can also hold or refuse an action before it runs.
QUESTIONS
The questions that come next
What does ED OCR's AI guidance require schools to check for?+
Nothing now. OCR's November 2024 resource, Avoiding the Discriminatory Use of Artificial Intelligence, was never binding, and the Department of Education has marked it formally rescinded. It described 21 situations where school AI use could lead to an investigation under Title VI, Title IX or Section 504, such as biased risk flags, proctoring software that misreads disability-related behavior, and AI-written 504 plans that no one reviewed. The underlying laws still apply, but since July 24, 2026 ED's Title VI rules reach only intentional discrimination. Read the full answer
How do we show OCR we measured our school AI for discrimination?+
No federal rule sets a required bias test for school AI, so there is no fixed format. Useful records include the tool's purpose and inputs, especially whether it uses race, sex or disability data; results compared across student groups; how staff review its outputs; accommodation requests and responses; and fixes made when problems appeared. Since July 24, 2026, ED's Title VI rules cover only intentional discrimination, but its Section 504 rules still bar methods that have the effect of discriminating on the basis of disability. Read the full answer
How do we show an early-warning / dropout-prediction model's results across cohorts?+
Compare the model's predictions and error rates for each student group that matters, such as race, sex, disability and English learner status, using the same data and time period. Report how often each group is flagged, how often flags are wrong in each direction, and how results change over time as the model or student population changes. Record what staff do with flags, because those decisions are what affect students. Repeat the check whenever the model, its inputs or its use changes. Read the full answer
SOURCES
Go back to the source
U.S. Department of Education, Office for Civil Rights (copy hosted by Connecticut DAS)
U.S. Department of Education
CHANGE LOG
What changed, and when
Rebuilt from primary sources; facts checked on this date.
Related: OCR AI Guidance for education AI workflows · NIST AI RMF · OMB M-25-21/22 · DoW RAI S&IP · EO 14179 · FIPS 140-3 · SHA-256 / FIPS 180-4