Shen Mingyan, Shen Yanping, Liu Fangchi, Jin Jiawen
Shulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, P. R. China.
Zhejiang Shuren University, Hangzhou, P. R. China.
BMC Nurs. 2025 Apr 29;24(1):470. doi: 10.1186/s12912-025-03115-8.
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.
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.
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.
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.
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.
Not applicable. This study did not involve a clinical trial.
生成式人工智能(GenAI)已成为护理教育中的一项强大工具,为增强临床推理、批判性思维和个性化学习提供了新方法。然而,关于人工智能生成内容的道德使用、数据隐私问题以及识别情感细微差别方面的局限性等问题仍然存在。
本研究旨在探讨护理专业学生如何利用GenAI工具制定护理计划,特别关注提示工程的创新作用。通过识别挑战和机遇,本研究旨在提供可行的见解,以便在保障人文护理技能的同时,将GenAI无缝整合到护理教育中。
采用定性设计,对一所院校的本科三年级护理专业学生进行半结构化访谈。参与者使用匿名临床病例和多种GenAI工具,强调提示的迭代设计以优化护理计划输出。对数据进行主题分析,以获取关于人工智能辅助学习和伦理考量的详细观点。
研究结果表明,GenAI工具提高了效率和概念清晰度,使学生能够更多地专注于高阶临床思维。提示工程显著提高了人工智能生成的护理计划的准确性和情境相关性。然而,学生们对回答不完整或不准确、GenAI有限的情感理解以及与敏感医疗数据相关的隐私风险表示担忧。当经过精心优化提示并进行批判性评估后使用时,GenAI被视为一种有价值的补充,而非人文护理能力的替代品。
本研究凸显了GenAI在护理教育中的变革潜力,强调了结构化提示工程和伦理保障的重要性。通过在技术创新与同理心、沟通和文化敏感性之间取得平衡,护理教育工作者可以利用人工智能深化临床推理,并使学生为未来的人工智能增强型实践做好准备。需要在不同环境中进行进一步研究,以验证这些发现并完善将GenAI整合到护理课程中的最佳实践。
不适用。本研究未涉及临床试验。