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This chapter examines strategies for scaling artificial intelligence (AI)-driven assessment systems across diverse educational contexts. It explores how AI can support large-scale implementation while maintaining accuracy, equity, and inclusivity. The chapter highlights key factors in scaling, including infrastructure requirements, stakeholder collaboration, and policy alignment. Challenges such as bridging the digital divide, ensuring ethical use, and managing costs are addressed alongside innovative solutions like open-source tools and cloud-based platforms. Through case studies and practical frameworks, this chapter provides a roadmap for deploying AI-driven assessment systems at scale to transform education and ensure equitable access for all learners.

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AI in High-Stakes and Standardized Testing

  • 章节

  • 发表日期：

    2025年

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  • Goran Trajkovski,
  • Heather Hayes

摘要

This chapter examines strategies for scaling artificial intelligence (AI)-driven assessment systems across diverse educational contexts. It explores how AI can support large-scale implementation while maintaining accuracy, equity, and inclusivity. The chapter highlights key factors in scaling, including infrastructure requirements, stakeholder collaboration, and policy alignment. Challenges such as bridging the digital divide, ensuring ethical use, and managing costs are addressed alongside innovative solutions like open-source tools and cloud-based platforms. Through case studies and practical frameworks, this chapter provides a roadmap for deploying AI-driven assessment systems at scale to transform education and ensure equitable access for all learners.