Bibliographic record
Abstract
When used consciously, critically, and with attention to ethical principles, the developing artificial intelligence (AI) technology tools provide effectiveness, speed, accuracy, and efficiency in meeting many needs of individuals, institutions, and organizations. It is essential to be artificial intelligence literate to recognize, understand, evaluate, and use artificial intelligence technologies effectively. This study aims to define artificial intelligence literacy, emphasize its importance, and examine the role of libraries in developing this literacy. In addition, it is to reveal the potential of artificial intelligence literacy to increase individuals’ ability to evaluate and use artificial intelligence technologies critically. Within the scope of the study, which was prepared with the qualitative description method and analyzed the relevant sources from domestic and foreign literature, the concept of artificial intelligence literacy was defined, the role of libraries in the development of this type of literacy was included, and the study was also supported with examples of the university and public libraries taken from the United States, Canada, Australia, and North America. The results obtained from the study’s findings indicate that the role of libraries in developing artificial intelligence literacy is crucial and that educational programs in this area should be strengthened. Libraries can guide the ethical and practical use of artificial intelligence technologies and enable individuals to benefit better from these tools. This research is considered to be original because there is no other study in the domestic literature that addresses the meaning of artificial intelligence literacy for libraries and evaluates the related foreign and domestic literature in a detailed and systematic way.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".