AI In Education

AI in education encompasses the deployment of machine learning, generative AI, and adaptive learning systems within schools, universities, and educational technology platforms. From an AI governance perspective, this domain raises critical compliance issues including student data privacy under FERPA and similar international regulations, algorithmic bias in grading and admissions systems, and the need for transparency when AI influences educational outcomes. Organizations implementing educational AI must establish clear policies around consent, data retention, model explainability, and human oversight to ensure these tools enhance rather than undermine equitable learning outcomes.

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Corporate PolicyUS2026-09-09

Microsoft's Contractual AI Safeguards for Schools Set a Vendor Governance Template

Microsoft has agreed to ten contractually enforceable AI safety and privacy commitments with the American Federation of Teachers and its New York City affiliate, covering student and educator data used in school AI deployments. The commitments include prohibitions on training AI models on student data, limits on data collection, plain-language family disclosures, bans on AI companion features, and mandatory human review for high-risk decisions. School districts can opt into these terms within existing contracts starting in November 2025.

Microsoft student data privacy AI in education contractual AI governance data minimization human oversight vendor due diligence