Prompts, privacy, and personalized learning: integrating AI into nursing education—a qualitative study
Open accessAbstract
BACKGROUND: Generative artificial intelligence (GenAI) has emerged as a powerful tool in nursing education, offering novel ways to enhance clinical reasoning, critical thinking, and personalized learning. However, questions remain regarding the ethical use of AI-generated outputs, data privacy concerns, and limitations in recognizing emotional nuances. OBJECTIVE: This study aims to explore how nursing students utilize GenAI tools to develop care plans, with a particular focus on the innovative role of prompt engineering. By identifying both challenges and opportunities, it seeks to provide actionable insights into seamlessly integrating GenAI into nursing education while safeguarding humanistic nursing skills. METHODS: A qualitative design was adopted, involving semi-structured interviews with third-year undergraduate nursing students at a single institution. Participants worked with anonymized clinical cases and multiple GenAI tools, emphasizing the iterative design of prompts to optimize care-plan outputs. Data were analyzed thematically to capture detailed perspectives on AI-facilitated learning and ethical considerations. RESULTS: Findings indicate that GenAI tools enhanced efficiency and conceptual clarity, allowing students to focus more on higher-order clinical thinking. Prompt engineering significantly improved the accuracy and contextual relevance of AI-generated care plans. However, students expressed concerns about incomplete or imprecise responses, GenAI's limited emotional understanding, and privacy risks associated with sensitive healthcare data. When used with careful prompt refinement and critical evaluation, GenAI was viewed as a valuable supplement rather than a replacement for humanistic nursing competencies. CONCLUSION: This study highlights the transformative potential of GenAI in nursing education, underscoring the importance of structured prompt engineering and ethical safeguards. By balancing technological innovation with empathy, communication, and cultural sensitivity, nursing educators can harness AI to deepen clinical reasoning and prepare students for future AI-enhanced practice. Further research across diverse settings is needed to validate these findings and refine best practices for integrating GenAI into nursing curricula. CLINICAL TRIAL NUMBER: Not applicable. This study did not involve a clinical trial.
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Despite China's leadership in AI innovation, vulnerabilities in interaction practices within nursing education remain insufficiently understood.
“Despite China’s leadership in AI innovation, vulnerabilities in interaction practices within nursing education remain insufficiently understood [40, 44].”
From From adoption to interaction: examining human-GenAI engagement patterns, ethical tensions, and educational trade-offs in nursing education (2026) · cites this paper as [44]
The Introduction > Background (page unavailable) says that research on how students use prompt engineering in nursing care planning is lacking and that applications of Chinese-language GenAI in nursing education remain understudied. It describes several high-performing Mandarin-language models, but does not establish China’s leadership in AI innovation. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThese perceptions align with broader scholarly concerns regarding analytical passivity and cognitive outsourcing [30,31,36,37].
“These perceptions align with broader scholarly concerns regarding analytical passivity and cognitive outsourcing [30,31,36,37].”
From Artificial Intelligence–Enabled Educational Tools and Clinical Reasoning in Undergraduate Nursing Education: A Systematic Review (2026) · cites this paper as [37]
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- Authors
- Shen, M., Shen, Y., Liu, F., & Jin, J.
- Affiliations
- Zhejiang Shuren University (CN)
- Hangzhou Normal University (CN)
- Corresponding author
- Shen, Mingyan
- Venue
- BMC Nursing
- Published
- 2025-04-29
- Volume
- 24
- Issue
- 1
- Pages
- 470-470
- ISSN
- 1472-6955 (ISSN-L)
- PMC
- PMC12042552
- OpenAlex
- W4409923866
- Cited by
- 76
- References
- 49
- Citations by year
- 2026: 65 · 2025: 11
- FWCI
- 18.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Nursing (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Department of Education of Zhejiang Province (JGBA2024419)
- 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, prompt engineering, care planning, data privacy, ethical use of AI, clinical reasoning, nursing students
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