Evidence map›Paper›PMID 41393174›Full record

ArticleJournal of multidisciplinary healthcare2025

Attitudes and Readiness for Artificial Intelligence Adoption Among Nursing Students in Saudi Arabia: A Cross-Sectional Study.

Fawaz Alatawi, Ali Kerari

Abstract read
In one paragraph

Article in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Fawaz AlatawiCommunity and Public Health Nursing, Ministry of Health, Tabuk, Saudi Arabia.
Ali KerariNursing Administration and Education Department, College of Nursing, King Saud University, Riyadh, Saudi Arabia.ORCID 0000-0003-1998-5367

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Although artificial intelligence (AI) has garnered increasing attention in education and healthcare, examining the attitude and readiness of future nurses toward AI integration is crucial for creating successful curricula and promoting appropriate utilization of this technology. Patients and Methods: This cross-sectional study was conducted among 227 nursing students at King Saud University using two validated instruments: the General Attitudes toward Artificial Intelligence Scale and the Medical Artificial Intelligence Readiness Scale for Medical Students. Responses were recorded using a 5-point Likert scale. Descriptive and inferential statistics, including t-tests and correlation analyses, were employed to examine differences across study variables. Results: Of the 227 participants, 48% (n = 109) were aged 18-24 years. Majority participants were women (60.4%, n = 137), and 39.6% (n = 90) were men. Nursing students generally demonstrated moderately positive attitudes toward AI. However, an independent-sample Conclusion: The findings of this study may help nursing colleges to incorporate AI into their curricula. However, the study's cross-sectional design, single site setting, and short duration limit causal inference and generalizability.

Indexed as

AI technologiesattitudeshealthcarenursing studentsreadiness

Identifiers

PMID41393174
PMCPMC12695703

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.