Learning science, instructional integrity, and human agency must guide all AI integration decisions.
Governance, data privacy, and algorithmic fairness are non-negotiable foundations for responsible AI deployment.
Digital equity, accessibility, and inclusion must remain central to prevent widening achievement gaps.
Comprehensive evaluation across critical dimensions of AI readiness in education
Learning science alignment, UDL, assessment integrity, teacher judgment
UNESCO principles, data privacy, bias mitigation, ethical frameworks
Strategic vision, change management, cross-functional coordination
Acceptable use policies, classroom boundaries, vendor governance
Teacher AI literacy, coaching systems, workforce readiness
AI-enhanced learning, human-AI collaboration, cognitive load
Digital divide, multilingual support, special education accessibility
KPIs, learning outcomes, continuous improvement loops
Each assessment generates a comprehensive, actionable implementation plan