A Taxonomy of AI Use for Learning in Higher Education

Authors

• J. Olmanson, University of Nebraska-LincolnFollow
• A. Hassani, University of Nebraska-Lincoln
• Minji Jeon, University of Nebraska-LincolnFollow

Date of this Version

6-2026

Document Type

Presentation

Citation

The Journal of Educational Communications and Technology [Special Issue: Proceedings of the Annual AECT International Convention 2025, Las Vegas, Nevada, October 20–24, 2025] (June 2026) 15.

doi: 10.59668/2579.26800

Comments

Open access

License: CC BY

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.