K-12 교육에서 AI 거버넌스에 대한 통일된 접근 방식

This IDC Perspective examines how unified AI governance can help K-12 districts harness AI to improve student outcomes, educator effectiveness, and operational efficiency while managing escalating legal, ethical, and security risks. It adapts IDC's unified AI governance model to K-12, outlining how strategy and oversight, organization and culture, core governance processes, and AI technology architecture work together to align AI use with student protections, community expectations, and global regulatory requirements. Drawing on case studies from leading districts worldwide, the report highlights practical steps for building cross-functional governance structures, establishing robust data and infrastructure foundations, balancing risk management with space for innovation, and embedding sustainability and responsible AI outcomes into everyday decision-making. It concludes with concrete recommendations for technology buyers on using procurement, support structures, and professional learning as levers to move from fragmented, reactive AI responses toward a coherent, districtwide governance framework."AI will not wait for schools to be ready, which is why K-12 leaders must treat governance as infrastructure, not insurance," says Matthew Leger, senior research manager, Worldwide Education and EdTech Digital Strategies, IDC. He also adds, "Districts that invest now in unified AI governance turn uncertainty into a strategic asset, building the trust, capacity, and technical foundations they need to scale AI in ways that are safe for students and sustainable for their communities."

Executive snapshot

• Key takeaways
• Recommended actions

Situation overview

• Governance as a core pillar of AI strategy
• Governance is not glamorous, but getting it wrong has real consequences
  • Why AI governance is especially challenging in K-12 education
  • IDC's unified framework for AI governance
• External forces driving the need for AI governance in higher education
  • The core of the unified framework: Strategy and oversight, plus organization and culture
    • Strategy and oversight: Setting direction and owning the risk
    • Organization and culture: Building the human machinery
    • Governance processes: Turning intent into practice
      • Assess: Discover, classify, and evaluate
      • Operate: Apply controls and enable use
      • Monitor: Measure, learn, and adjust
  • AI technology architecture: Data, platforms, applications, and infrastructure
    • Data: The foundation that cannot be skipped
    • AI platforms: Institutional control as a governance tool
    • Applications: Governing what AI does in classrooms and operations
    • Infrastructure: Compute, devices, and networks
  • The importance of innovation enablers in AI governance
  • The importance of sustainability in AI governance

Advice for the technology buyer

• Build governance into strategy from the start
• Move from fragmented to unified governance
• Prioritize data and infrastructure foundations
• Balance risk management and innovation
• Invest in people as much as technology
• Use procurement as a governance lever
• Stand up dedicated support and expertise for AI governance

Learn more

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