AI in Education
Educational institutions are deploying AI for admissions screening, student assessment, adaptive learning platforms, academic integrity monitoring, and administrative automation. These systems process sensitive data about minors, make high-stakes determinations about students' educational futures, and operate in an environment with strong data protection obligations under FERPA, COPPA, and the EU AI Act. Several AI applications in education are classified as high-risk under EU law, triggering conformity assessment and transparency requirements.
Key board-level questions
- 1.Do our AI-assisted admissions and assessment tools comply with anti-discrimination law and EU AI Act high-risk obligations?
- 2.Are we handling student data — including data about minors — in compliance with FERPA, COPPA, and applicable data protection law?
- 3.How do we ensure meaningful human oversight over AI systems that affect students' academic records or institutional standing?
- 4.Have we audited AI tools for bias that could disadvantage students based on demographic characteristics?
Regulatory frameworks
EU AI Act: AI Literacy and Prohibited AI Systems Provisions (Applicable 2 February 2026)
The EU AI Act's first major compliance deadline takes effect on 2 February 2026, requiring all organizations that develop or deploy AI within the EU to establish AI literacy measures for their workforce. As of this date, the Act's prohibitions on AI systems deemed to pose unacceptable risks also become enforceable. Organizations must have ceased operation of any prohibited AI practices and demonstrated adequate staff competency with AI systems by this date.
UK ICO Guidance on Artificial Intelligence and Data Protection
The UK ICO's guidance on AI and data protection establishes how the UK GDPR and Data Protection Act 2018 apply to the design, development, and deployment of AI systems that process personal data.
Colorado AI Act SB205
Colorado's SB 205 is the first US state statute imposing affirmative obligations on developers and deployers of high-risk AI systems, requiring algorithmic impact assessments, transparency notices, and consumer rights for consequential decisions.
NIST Artificial Intelligence Risk Management Framework Playbook
Voluntary, use-case-agnostic operational companion to the NIST AI Risk Management Framework (AI RMF 1.0) that provides structured, actionable guidance, suggested actions, and example outputs for implementing the four core AI RMF functions-GOVERN, MAP, MEASURE, and MANAGE-across the AI system lifecycle.
ISO/IEC 42001:2023 – Information Technology – Artificial Intelligence – Management System
The first internationally certified AI management system standard, providing a structured framework for establishing, implementing, maintaining, and continually improving an organization's AI management system (AIMS), with certifiable requirements applicable to any organization that develops, provides, or uses AI-based products and services.