Reimagining Library Services in the Age of AI: A Case Study from a Canadian Academic Library
Bibliographic record
Abstract
As artificial intelligence reshapes higher education, academic libraries are reimagining their services to meet emerging challenges and opportunities. This article presents a case study of the University of Manitoba Libraries (UML), a Canadian academic library actively integrating AI to improve service delivery, enrich research support, and enhance user engagement while upholding core values of access, equity, and scholarly integrity. Four key initiatives are discussed: (1) the development of an in-house AI chatbot built on Microsoft Azure services, offering real-time conversational assistance grounded in trusted library content; (2) the implementation of Ex Libris’ Primo Research Assistant to enhance discovery with GPT-based responses; (3) an exploratory research project applying Retrieval-Augmented Generation (RAG) to the institutional repository, MSpace, using metadata embeddings to improve access to open scholarship; and (4) the redesign of the science library as a future-ready, AI-enabled space featuring distributed service points, intelligent kiosks, and teaching labs for AI literacy. Together, these initiatives illustrate how AI can be adopted not as a replacement for human expertise, but as a tool for strategic innovation. The article reflects on the motivations, development processes, and ethical considerations guiding UML’s work, offering insights for libraries seeking to define their own responsible, user-centered AI trajectories.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.048 | 0.017 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".