Knowledge and attitudes of nursing students towards artificial intelligence and related factors: A systematic review
Bibliographic record
Abstract
This systematic review aimed to evaluate the knowledge and attitudes of nursing students towards artificial intelligence (AI) and to identify factors associated with their perspectives. A comprehensive search was conducted across various international electronic databases, including Scopus, PubMed, and Web of Science. Keywords were derived from Medical Subject Headings (MeSH) and included terms such as "knowledge", "attitude", "artificial intelligence", and "nursing students". The search encompassed records from the earliest available date up to July 20, 2024. The selected studies were evaluated for quality using the Appraisal tool for Cross-Sectional Studies (AXIS tool), an appraisal instrument designed for cross-sectional studies. In total, 1,299 nursing students were included across six cross-sectional studies. Among the participants, 80.87% were female, with a mean age of 22.01 (standard deviation [SD]=3.05) years. The studies incorporated in this systematic review were conducted in Egypt (n=3), India (n=1), the United States (n=1), and Croatia (n=1). The average knowledge score about AI among nursing students in the three studies was 66.62 out of 100, reflecting good knowledge. Students' knowledge of digital transformation, digital skills, and digital health literacy had a significant positive relationship (n=1). The average positive attitude of nursing students towards AI, as observed in six studies, was 64.73 out of 100, indicating a generally high positive attitude among nursing students regarding AI. There was a significant relationship between male gender (n=1), adoption of AI technology, and knowledge (n=1) with a positive attitude about AI. Nursing policymakers and managers can enhance nursing students' knowledge and attitudes toward AI by focusing on digital transformation, digital skills, digital health literacy, male gender, AI technology adoption, and knowledge.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".