How to incorporate artificial intelligence (AI) into your library workflow
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
Purpose This paper aims to highlight the recent developments in artificially intelligent chatbots and how the resulting tools can be incorporated into the daily workflow of library work. Design/methodology/approach Recent literature is examined, parallels to librarian reactions to the birth of the original internet search engines are drawn and suggestions for the use of specific tools for specific tasks are given. Findings Although effectively less than 6 months old, the field of artificial intelligence (AI) chatbots is already fulsome enough to be able to be usefully incorporated into the profession. More tools are imminent, though each of them does and will continue to have shortcomings of which informational professionals need to be aware. Practical implications This paper provides practical suggestions and specific tools to incorporate into the workflow of different library specialties, along with important caveats for quality and bias. Social implications The public has adopted the use of AI chatbots faster than any previously introduced technology. Librarians have a history of moving more slowly when it comes to the core values of the profession, such as information searching. It is vital for information professionals, such as librarians, to understand both the value and the pitfalls of these tools to be able to work with patrons and stay relevant in the eyes of the public and institutional funders. Originality/value This paper fills a need for practical advice in using AI to perform daily library work.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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