Navigating the Generative AI Revolution: The Role of Academic Librarians within Higher Education Institutions
Bibliographic record
Abstract
In the rapidly evolving landscape of generative AI (GenAI), academic librarians stand at the forefront of navigating these advancements within the university setting. The emergence of GenAI tools has the potential to revolutionize how we approach library instruction, underscoring the critical role of librarians in guiding students and faculty through the strategic and ethical utilization of these technologies. In this presentation, I will explore the pedagogical strategies I've employed in classroom discussions about GenAI tools, drawing from my experiences at the University of Ottawa. I will share insights into the diverse reactions and valuable feedback received from both students and faculty, reflecting on how these interactions have shaped my approach to exploring this topic in the classroom. Furthermore, I will examine how the GenAI revolution presents an unparalleled opportunity for academic libraries and will identify five areas where academic librarians’ roles are being impacted or evolving or where new considerations are being introduced.
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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 | 0.034 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.038 | 0.060 |
| Scholarly communication | 0.046 | 0.023 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".