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This chapter introduces the ongoing Adaptation and Refinement phase necessary after the initial scaling of Generative Artificial Intelligence (GenAI) in Higher Education Institutions (HEIs). As AI tools become embedded in daily operations, this phase requires continuous adjustments at cultural and procedural levels, often moving at the speed of organizational change rather than technology alone (Deloitte, 2025). It requires embedding AI thoughtfully, ethically, and sustainably across the institution (Cornell University Task Force on GenAI in Academic Research, 2023; Maneval, 2025; UNESCO, 2023). This signifies a shift towards a mature state where AI integration is constantly evaluated, improved, and strategically aligned to create demonstrable value (Relyea et al., 2024). The distinction between this phase and the preceding Scaling phase (Chap. 14 ) is crucial; while scaling focuses on broadening the reach of proven AI, adaptation centers on sustaining effectiveness, optimizing existing AI use, and evolving practices in response to ongoing learning and environmental shifts (see Table 15.1).

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Ongoing Adaptation and Refinement (Phase 4)

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  • 发表日期：

    2026年

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  • David Woodring

摘要

This chapter introduces the ongoing Adaptation and Refinement phase necessary after the initial scaling of Generative Artificial Intelligence (GenAI) in Higher Education Institutions (HEIs). As AI tools become embedded in daily operations, this phase requires continuous adjustments at cultural and procedural levels, often moving at the speed of organizational change rather than technology alone (Deloitte, 2025). It requires embedding AI thoughtfully, ethically, and sustainably across the institution (Cornell University Task Force on GenAI in Academic Research, 2023; Maneval, 2025; UNESCO, 2023). This signifies a shift towards a mature state where AI integration is constantly evaluated, improved, and strategically aligned to create demonstrable value (Relyea et al., 2024). The distinction between this phase and the preceding Scaling phase (Chap. 14 ) is crucial; while scaling focuses on broadening the reach of proven AI, adaptation centers on sustaining effectiveness, optimizing existing AI use, and evolving practices in response to ongoing learning and environmental shifts (see Table 15.1).