ArticleFrontiers in public health2025
The strengths, weaknesses, opportunities, and threats of generative artificial intelligence: a qualitative study of undergraduate nursing students.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Meta-synthesis of nursing students' experiences with generative artificial intelligence-assisted learning.Frontiers in medicine · 2026Pooled it
- Health Professional Students' Use of Generative Artificial Intelligence During Clinical Placements: Cross-Sectional Online Survey Study.JMIR medical education · 2026Article
- The experience of nursing students using generative artificial intelligence: a qualitative meta-synthesis.BMC nursing · 2026Article
- Undergraduate nursing students' attitudes and needs regarding the use of generative artificial intelligence in professional learning: a qualitative study.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: While Generative Artificial Intelligence (Gen AI) is increasingly applied in nursing education, research on undergraduates' perceptions, experiences, and impacts remains limited. Objective: This study aims to explore undergraduate nursing students' perceptions of the strengths, weaknesses, opportunities, and threats (SWOT) associated with Gen AI through qualitative research methods. Methods: Using the SWOT analysis framework as the theoretical basis, data were collected through semi-structured interviews with nursing undergraduates via convenience sampling from May to July 2025 until saturation, and analyzed using Colaizzi's phenomenological method for thematic extraction. Results: A total of 36 nursing undergraduates were interviewed, from whom four main themes and 16 sub-themes were identified. These were categorized into internal and external factors. Internal positive factors (Strengths) included personalized learning assistance, skill training and curriculum support, efficiency and cognitive expansion, and data processing and learning capability. Internal negative factors (Weaknesses) involved ethical and legal risks, the generation of low-quality or inaccurate outputs, technical barriers, and cognitive and learning risks. External opportunities comprised policy and resource support, technological advancement and evolution, interdisciplinary integration and collaboration, and emerging career opportunities. External threats included technological adaptation and cost risks, digital divide and equity gap, job displacement risk, and educational integrity risk. Conclusion: Undergraduate nursing students regard generative AI as a double-edged sword-its strengths in boosting learning efficiency, broadening knowledge access and simulating clinical decisions are offset by ethical, technological and equity challenges. Nursing education must therefore strengthen technical guidance, ethics training and resource optimization to maximize its strengths and opportunities while minimizing its weaknesses and threats.
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Registered trials
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.