学术速览 | 护理教育者对AI的认知：职能边界、应用限度与深远影响综述

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Title

Nursing educators' perspectives on artificial intelligence: A rapid review of roles, limits, and implications

护理教育工作者对人工智能的看法：角色、局限与影响的快速综述

Keywords

AI; Educators; Nursing; review

人工智能；教育工作者；护理；综述

Background

Artificial intelligence (AI) is rapidly entering academic nursing education, yet its integration remains uneven and often lacks pedagogical guidance. Understanding how nursing educators perceive AI's role is critical to ensuring its appropriate and effective use.

人工智能（AI）正以前所未有的速度渗透至学术护理教育中，但其应用程度参差不齐，且普遍缺少相应的教学法指引。深入把握护理教育者对AI功能的理解，对于推动其规范且高效地落地具有关键意义。

Aim

This rapid review aimed to synthesize current evidence on nursing educators' perceptions of AI in academic nursing education, with a focus on identifying which educational tasks can be enhanced, replaced, or are not amenable to AI.

本快速综述力求整合现有研究中关于护理教育者对AI在学术护理教育中所持看法的证据，着重厘清哪些教学环节可借助AI加以强化、哪些可被替代、以及哪些领域不适于AI介入。

Methods

A rapid review was conducted using a multimethod search strategy that combined AI-assisted semantic searching, structured database searches, targeted journal and reference list searching, and manual verification. Eligible studies reporting nursing educators' perspectives on AI in academic settings were synthesized using descriptive and directed content analyses informed by predefined domains, while remaining open to emergent themes.

本研究采用多元检索方式开展快速综述，将AI驱动的语义检索、规范化的数据库查询、针对性期刊及参考文献清单检索与人工核对相结合。对满足纳入条件的研究实施了描述性与定向内容分析，分析依托预先设定的领域范畴，同时为新涌现的主题保留开放空间。

Results

A total of 55 studies were included in this review.Educators consistently view AI as augmenting rather than replacing faculty roles. AI is perceived as most effective in simulation-based learning and personalized tutoring, followed by feedback and assessment, curriculum design and administrative work, and academic writing and research support.Bounded tasks, including administrative drafting, grading and the summarizing of narrative data, may be partially substituted, though faculty oversight remains necessary.In contrast, relational, ethical and judgment-based domains, including empathy, moral reasoning, complex clinical judgment, hands-on clinical practice and faculty mentorship, are not considered substitutable.Workload effects are directionally mixed: AI reduces time spent on bounded tasks, but verifying its outputs generates new demands. Perceptions vary with prior exposure, which correlated with trust in AI, and with age, gender, academic rank and nationality.Key barriers include limited training, ethical concerns and infrastructure gaps.

本综述最终纳入55项研究。教育者普遍将AI视为对师资职能的辅助与增强，而非取而代之。在各类应用场景中，AI在模拟教学与个性化辅导领域被公认为效能最佳，其后依次为反馈与评价、课程规划与行政事务、以及学术写作与研究支撑。行政文稿起草、评分作业与叙述性资料归纳等界定清晰的任务可实现部分替代，但教师把关仍不可或缺。反观涉及人际互动、伦理思辨与判断决策的范畴——如共情能力、道德推论、复杂临床判断、临床实操以及师者引导——则被认定无法由AI取代。工作负荷的影响呈现双向特征：AI缩减了界定任务所耗费的时间，但核验其产出又催生了新的工作量。看法因先前接触程度而分化，该因素与对AI的信赖度挂钩，并受年龄、性别、学术职级及国别背景左右。主要阻力源自培训欠缺、伦理忧患与基础设施短板。

Conclusion

AI's educational impact depends less on technological capability than on pedagogical design, faculty preparedness, and governance. Evidence from resource-constrained settings indicates that these preconditions are themselves unevenly distributed, highlighting the need for structured implementation strategies that address infrastructure and verification burden alongside pedagogy.

AI对教育领域的影响程度，相较于技术本身的先进性，更受制于教学架构设计、教育者的能力储备以及制度治理水平。来自资源受限环境的证据揭示，这些先决条件自身的分布同样失衡，凸显出亟需构建系统化的推行路径，统筹应对基础设施、审核负荷与教学法等多元议题。

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