Evidence map›Paper›PMID 40301862›Full record

ArticleBMC nursing2025

Prompts, privacy, and personalized learning: integrating AI into nursing education-a qualitative study.

Mingyan Shen, Yanping Shen, Fangchi Liu, Jiawen Jin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

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 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 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

4 authors.

Mingyan ShenShulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, P. R. China. 289155169@qq.com.
Yanping ShenShulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, P. R. China.
Fangchi LiuHangzhou Normal University, Hangzhou, 310010, P. R. China.
Jiawen JinZhejiang Shuren University, Hangzhou, P. R. China.

Funding

Department of Education of Zhejiang Province JGBA2024419
6 · The paper itself

Abstract

backgroundGenerative 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.

objectiveThis 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.

methodsA 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.

resultsFindings 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.

conclusionThis 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.

Indexed as

Care plansClinical reasoningEthical considerationsGenerative artificial intelligenceHumanistic skillsNursing educationPrompt engineering

Identifiers

PMID40301862
PMCPMC12042552

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.