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Case study demonstrates how human–AI collaboration aligns course goals and structures in undergraduate education, highlighting the complementary roles of faculty judgment and machine analytics.
This study introduces the Continuous Alignment Framework, showing how higher education leadership supports ongoing collaboration between faculty expertise and AI across a semester. Evidence from one undergraduate course demonstrates that AI offers analytical insight while faculty provide contextual judgment shaped by discipline, classroom experience, and student interaction. Together, they help maintain alignment among course structure, learning goals, and student expectations. The framework offers a concise conceptual explanation of productive human–AI collaboration in teaching and learning.
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Mopelola Fatile (2026) studied this question.
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