Evidence map›Paper›PMID 40597186›Full record

ArticleBMC nursing2025

Insights into the relationship between anxiety and attitudes toward artificial intelligence among nursing students.

Ahmad Ayed, Moath Abu Ejheisheh, Rasmieh Al-Amer, Ibrahim Aqtam, Amira Mohammed Ali, Elham H Othman, Mosaab Farajallah, Jamal Qaddumi, Ahmad Batran

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Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
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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

28 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

9 authors.

Ahmad AyedFaculty of Nursing, Arab American University, Jenin, Palestine.
Moath Abu EjheishehDepartment of Nursing, Faculty of Allied Medical Sciences, Palestine Ahliya University, Bethlehem, Palestine.
Rasmieh Al-AmerFaculty of Nursing, Yarmouk University, Irbid, Jordan.
Ibrahim AqtamDepartment of Nursing, Ibn Sina College for Health Professions, Nablus University for Vocational and Technical Education, Nablus, Palestine. ibrahim.aqtam@nu-vte.edu.ps.
Amira Mohammed AliDepartment of Psychiatric Nursing and Mental Health, Faculty of Nursing, Alexandria University, Smouha, Alexandria, 21527, Egypt.
Elham H OthmanFaculty of Nursing, Applied Science Private University, Amman, Jordan.
Mosaab FarajallahDepartment of Nursing, Faculty of Allied Medical Sciences, Palestine Ahliya University, Bethlehem, Palestine.
Jamal QaddumiFaculty of Medicine and Health Sciences, An-Najah National University, Nablus, Palestine.
Ahmad BatranDepartment of Nursing, Faculty of Allied Medical Sciences, Palestine Ahliya University, Bethlehem, Palestine.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial Intelligence (AI) integration in healthcare education represents a critical technological advancement that requires careful examination of student preparedness and acceptance. In the Palestinian context, limited research exists on nursing students' psychological responses to AI implementation, despite growing global emphasis on AI competency in healthcare professions. Understanding the relationship between anxiety and attitudes toward AI is essential for developing effective educational strategies that can facilitate successful technology adoption while addressing cultural and contextual barriers specific to the Palestinian healthcare education environment.

introductionArtificial Intelligence (AI) integration in nursing education remains underexplored in the Palestinian context, where limited research addresses students' anxiety and attitudes toward AI. This study examines this relationship to fill a critical gap and inform culturally relevant strategies for AI adoption in healthcare education.

methodsA cross-sectional study was conducted among 264 nursing students at Palestine Ahliya University (2024-2025). Validated scales (AI Anxiety Scale, SATAI) assessed anxiety and attitudes. We analyzed data via correlation and regression using SPSS v26.

resultsHigh AI anxiety (mean = 80.3, SD = 9.4) contrasted with positive attitudes (mean = 114.3, SD = 12.8). Regression identified attitude as the strongest predictor of anxiety (B = 5.171, p < .001), alongside younger age, female gender, and non-use of AI. Academic year and AI education showed no significant effects.

conclusionNegative attitudes and limited AI exposure drive anxiety, particularly among younger females and non-users. To mitigate this, we recommend integrating AI literacy modules into curricula, fostering hands-on AI experiences, and designing gender-sensitive training. These findings emphasize the urgency of addressing sociocultural and educational barriers to AI readiness in Palestinian nursing education. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

AnxietyArtificial intelligenceAttitudeNursing studentsPalestine

Identifiers

PMID40597186
PMCPMC12211391

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LicenceCC BY
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Registered trials

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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.