A Taxonomy of AI Use for Learning in Higher Education

Abstract

As generative AI becomes a familiar and widely used tool for completing academic tasks, instructors and instructional designers face the challenge of aligning its use with course objectives, major assignments, and university academic integrity policies. Drawing on prior research, we identify six categories of student engagement with AI during course-related independent learning. We frame our organization of these categories as a way for instructional designers to anticipate learner usage across assignments, promote learning-centered interactions with AI, and design or redesign assignments accordingly. The taxonomy supports learning-centered approaches for integrating AI into planning, studying, research, and course assignment completion.

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