Fair use is a legal doctrine in U.S. copyright law that permits limited use of copyrighted material without permission from the rights holder for purposes such as education, research, scholarship, commentary, criticism, and news reporting. Fair use determinations are context-specific and rely on an analysis of four factors: (1) the purpose and character of the use, (2) the nature of the copyrighted work, (3) the amount used, and (4) the effect on the market value of the original work.

The growth of artificial intelligence (AI) has complicated traditional understandings of fair use, raising new legal and institutional questions about the use of copyrighted materials.  In digital learning, research, and AI-enabled environments, fair use plays a critical role in enabling access, innovation, and knowledge sharing while balancing the rights of content creators. AI complicates fair use practices because it raises unresolved questions such as:

• Whether training on copyrighted works constitutes fair use?
• Whether AI outputs are “transformative”?
• Who bears responsibility when outputs resemble copyrighted material?
• How should attribution, licensing, and compensation work at scale?

Courts have historically emphasized human purpose and transformation. AI introduces non-human intermediaries, probabilistic reuse, and scale far beyond traditional educational copying. This matters for educators, researchers, libraries, and publishers because fair use is no longer just about what humans copy: it is about what systems ingest, how outputs are generated, and how institutions manage risk, disclosure, and compliance.