Academic AI Impact Study

As AI becomes embedded in research and learning environments, academic libraries are exploring its operational implications. Questions about efficiency, quality, and the role of human expertise are moving from theoretical to practical.

This study contributes case-based evidence to that discussion. Based on in-depth interviews with library professionals at 8 institutions, it examines how Academic AI affect two core workflows: course reading list preparation and metadata creation. The findings document measurable changes in service capacity and the allocation of professional effort.

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Key findings

30–60%

reduction in time spent on manual, repetitive work

2–4x

capacity increase of cataloging and course resource readiness, without adding staff

50-60%

of reading lists immediately available after AI processing

70–90%

of AI-generated output accepted with minor edits

About the research

The report examines Leganto Syllabus Assistant and Alma Metadata Assistant through interviews with library professionals.

Featured case studies:

Brock University: Recovering course readiness during a period of significant operational pressure
Flagship R1 University: From reactive to proactive creation of course resource lists
Universidad Tecnológica de Bolívar: Large-scale retrospective metadata enrichment
Large Research University: Improving metadata quality beyond minimal cataloging

Research conducted by Emerging Strategy on behalf of Clarivate.