From Learning Tool to Teaching Partner: How Librarians Use Generative AI to Support Research Across Disciplines
Bibliographic record
Abstract
Librarians in academic, public, and school settings frequently encounter reference questions outside their subject expertise. They also recognize the need for members of the profession to be arbiters of artificial intelligence (AI) in the information landscape and to be among the first to use this technology so that they can effectively advise and teach others how to best do the same.1 As generative artificial intelligence (GenAI) tools reshape reference services across library contexts, they offer librarians both a way to address their knowledge gaps and a means to support student learning through instruction. In these instances, GenAI has emerged as a valuable tool for reference and instruction, allowing librarians to quickly build foundational knowledge, identify relevant terminology, and provide more effective research support. Through librarianship training, we have a responsibility to support library users in bridging knowledge gaps and
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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 | 0.036 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.032 | 0.038 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".