The strengths, weaknesses, opportunities, and threats of generative artificial intelligence: a qualitative study of undergraduate nursing students
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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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When grounded on AI-produced reflective text without oversight by teachers, reflective practice tends to culminate in stapled and shallow reflections.
“However, when grounded on AI-produced reflective text without any oversight by teachers, this kind of practice tends to culminate in stapled and shallow reflections [19].”
From HOW AI TOOLS (LIKE CHATGPT/GEMINI) AFFECT CRITICAL THINKING IN NURSING STUDENTS. A SYSTEMATIC REVIEW (2026) · cites this paper as [19]
Results, Theme 2 (page unavailable) reports students’ concerns that relying on AI can foster shortcut thinking, and the Discussion, ST strategy (page unavailable) says a lack of human review can compromise AI-generated content quality. The paper does not report that teacher-unsupervised AI-produced reflective text tends to yield stapled or shallow reflections specifically. (LLM, conf=0.82)
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- Authors
- Yuan, Y., Fu, J., Leng, L., Wen, Z., Wei, X., Han, D., Hu, X., Liang, Y., Luo, Q., Zhang, X., & Hu, R.
- Affiliations
- Zunyi Medical University (CN)
- Zunyi Medical and Pharmaceutical College (CN)
- Corresponding author
- Hu, Rujun
- Venue
- Frontiers in Public Health
- Publisher
- Frontiers Media SA
- Published
- 2025-09-04
- Volume
- 13
- Pages
- 1672140-1672140
- ISSN
- 2296-2565 (ISSN-L)
- PMC
- PMC12443828
- OpenAlex
- W4413998539
- Cited by
- 25
- References
- 53
- Citations by year
- 2026: 23 · 2025: 2
- FWCI
- 6.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Public Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Recruitment Program of Global Experts
- Division of Undergraduate Education
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- generative AI, nursing education, undergraduate nursing students, student perceptions, SWOT analysis, ethical risks of AI, digital divide, job displacement, personalized learning, artificial intelligence in education
- MeSH terms
- Generative Artificial Intelligence, Adult, Artificial Intelligence, Curriculum, Education, Nursing, Baccalaureate, Female, Humans, Interviews as Topic, Male, Students, Nursing, Qualitative Research, Young Adult
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