A Taxonomy of Generative AI Use for Course-Related Independent Learning in Higher Education

Abstract

Generative Artificial Intelligence (AI) is being integrated into higher education faster than educators and institutions can develop clear guidelines for effective, ethical use. As a result, students and educators face uncertainty and inconsistency about how to meaningfully incorporate AI into learning. To address this gap, we examine how undergraduate and graduate students use generative AI during course-related independent learning—the learner-managed, out-of-class work through which students interpret assignments, manage learning processes, and complete academic tasks. Drawing on qualitative data from two design-based research studies, we identify and categorize six patterns of student engagement with generative AI: Not for Me, Escape, Get Me Going, Feedback Please, Help Me Learn, and Magnify. These categories map a continuum ranging from AI abstinence, to task avoidance via automation, through process support, to learning amplification. The resulting taxonomy offers an empirically-based, descriptive framework for understanding learner-initiated uses of AI and provides practical guidance for students and educators seeking to realize strategic, ethical, learning-centered engagement with generative AI in higher education.

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