The Rise of Artificial Intelligence in Academic Libraries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current uses of artificial intelligence (AI) in academic libraries across the globe are explored. The uses of AI are then weighed against the concerns about the adoption of AI in academic libraries. Uses of AI in academic libraries include AI-powered chatbots used in reference services, and generative AI (GenAI) like ChatGPT used by librarians during information literacy classes. In addition, some libraries hold AI-themed lectures and forums. Concerns of AI include start-up costs & time and resource commitment, lack of staff training, concerns over job cuts, furthering bias, privacy concerns, copyright concerns, and conflict with the values of librarianship. Finally, recommendations for the field include the need for AI policies and guidelines from academic libraries to guide staff, transparency, and the implementation of AI literacy instruction in information literacy classes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it