Bridging the AI gap: Comparative analysis of AI integration, education, and outreach in academic libraries
Bibliographic record
Abstract
Generative artificial intelligence (AI) applications, such as ChatGPT, Bard, Gemini, and Copilot, have revolutionized education, capturing the attention of faculty, administration, and students alike. Academic libraries have actively engaged in facilitating the use of AI technologies while addressing challenges like misinformation, academic integrity concerns, and ethical considerations. This study examines AI integration, education, and outreach in academic libraries across Europe, North America (Canada and USA), Sub-Saharan Africa, Latin America and the Caribbean. An environmental scan of 40 academic library websites from the Times Higher Education 10 highest-ranked libraries in each region was conducted. Results show that more than 50% of the libraries offered educational materials and 42.5% conducted educational activities, while only 12.5% included AI policies. The study results demonstrate that although many libraries have begun to integrate AI into their services, significant differences exist between regions in the Northern and Southern Hemispheres.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".