Evidence map›Paper›PMID 41519757›Full record

ArticleBMC nursing2026

Empowering nursing students during AI era: educational strategies for enhancing knowledge and acceptance of artificial intelligence.

Amel Dawod Kamel Gouda, Marwa Samir Sorour, Amany Salama Ayoub, Reda M Nabil Aboushady, Mai Nour Eldien Mohamed Mohamed Awad, Fatma Mohamed El Swerky, Manal Mohamed Ahmed Ayed, Basma W Elrefay, Hend Abdelmonem Eid Elshnawie, Heba Taha Mahmoud Elsayed

Abstract read
In one paragraph

Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

10 authors.

Amel Dawod Kamel GoudaMaternal and Newborn Health Nursing, Faculty of Nursing, Cairo University, Cairo, Egypt.
Marwa Samir SorourNursing Administration, Faculty of Nursing, Tanta University, Tanta, Egypt. marwa_serour@nursing.tanta.edu.eg.
Amany Salama AyoubNursing Education, Faculty of Nursing, Cairo University, Cairo, Egypt.
Reda M Nabil AboushadyMaternal and Newborn Health Nursing, Faculty of Nursing, Cairo University, Cairo, Egypt.
Mai Nour Eldien Mohamed Mohamed AwadFellow of Community Health Nursing, Specialized Medical Hospital, Mansoura University, Mansoura, Egypt.
Fatma Mohamed El SwerkyFamily and Community Health Nursing, Faculty of Nursing, Port Said University, Port Said, Egypt.
Manal Mohamed Ahmed AyedPediatric Nursing, Faculty of Nursing, Sohag University, Sohag, Egypt.
Basma W ElrefayObstetrics and Gynecology Nursing, Faculty of Nursing, Delta University for Science and Technology, Gamasa, Egypt.
Hend Abdelmonem Eid ElshnawieMedical-Surgical Nursing, Faculty of Nursing, Alexandria University, Alexandria, Egypt.
Heba Taha Mahmoud ElsayedGeriatric Nursing, Ibn Sina National College of Medical Science, Cairo, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is rapidly permeating health systems, yet undergraduate nursing students often report limited AI literacy and uncertainty about safe, appropriate use.

aimTo evaluate the effect of a standardized 10-session blended curriculum on nursing students’ AI knowledge and acceptance and to examine theory-consistent associations between knowledge and acceptance.

designOne-group pretest–posttest quasi-experimental study.

methodsA stratified random sample of undergraduates (n=1,000) from the Faculty of Nursing, Sohag University (Egypt) completed a self-administered questionnaire at baseline and one month after the program. Outcomes were the AI Knowledge Scale (AIKS-16; 0–32) and the AI Acceptance Scale (AIA-34; 0–136) aligned with Technology Acceptance Model subdomains (Perceived Usefulness [PU], Perceived Ease of Use [PEOU], Attitude/Intention). Analyses used paired t-tests with 95% CIs and Cohen’s d; Pearson correlations; and exploratory ANCOVA adjusting for baseline to probe subgroup differences (sex, residence). Ethics approval: IRB 88-6-2023.

resultsKnowledge increased from 15.01±4.72 to 30.33±3.11 and acceptance from 67.02±13.47 to 122.33±9.21 (both p<0.001; large effects). Gains were observed across PU, PEOU, and Attitude/Intention. Post-test knowledge correlated strongly with acceptance (r=0.647, p<0.001). ANCOVA showed no educationally meaningful differences by sex or residence after adjustment (partial η2 ≤0.006). Knowledge-level transitions indicated marked movement from low/moderate to high categories.

conclusionsA standardized, fidelity-checked blended curriculum produced substantial and equitable improvements in AI knowledge and acceptance among undergraduate nursing students. Framed by the Technology Acceptance Model, results suggest that concept scaffolding, brief hands-on practice, and explicit disclosure/verification routines strengthen perceived usefulness and ease of use, supporting accountable AI adoption. Multi-site controlled studies with performance-based outcomes and longer follow-up are warranted.

trial registrationNot applicable. This was an educational pretest–posttest study with no clinical trial component.

Indexed as

AcceptanceArtificial intelligenceNursing educationUndergraduate students

Identifiers

PMID41519757
PMCPMC12849734

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.