Views of Academic Library Directors on Artificial Intelligence: A Representative Survey in Hungary
Bibliographic record
Abstract
Artificial intelligence (AI) is a defining technology of the 21st century, creating new opportunities for academic libraries. The goal of this paper is to provide a much-needed analysis, interpreted in an international context, on what the leaders of academic libraries in East-Central Europe, and specifically in Hungary, think about AI and its implementation in a library setting. The survey shows that according to library directors AI is more of an opportunity for academic libraries than a threat, and it could provide support in all areas of library operation, including digitising, information service, and education. Findings indicate that a quarter of the Hungarian academic libraries surveyed use AI-supported solutions, mostly in the areas of information retrieval and data processing. Using Rogers (The diffusion of innovations. 5th ed. The Free Press, 2003) diffusion of innovation model, it may be projected that an explosive growth is to be expected in the use of AI in libraries.
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How this classification was reachedexpand
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".