Generative AI and intelligent tutoring systems in nursing education: A systematic review of impacts on higher-order thinking skills and clinical competency

IF 4 3区医学 Q1 NURSING

Nurse Education in Practice Pub Date : 2026-05-01 Epub Date: 2026-04-28 DOI:10.1016/j.nepr.2026.104847

Hafizs Nasirun , Sigit Mulyono , Ayu Widowati Dwi Hapsari , Hendri Hardi Wiradinata , Mustika Ratu

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引用次数: 0

Abstract

Aims

This review synthesizes the impact of Artificial Intelligence (AI) on nursing students' clinical competency, Higher-Order Thinking Skills (HOTS) and educational outcomes to differentiate between procedural efficiency and deep cognitive retention.

Background

Generative AI and Intelligent Tutoring Systems (ITS) offer personalized scaffolding. However, their comparative efficacy against conventional pedagogical approaches, such as face-to-face lectures and standard non-AI simulations, remains controversial, particularly regarding the potential trade-off between skill acquisition and cognitive depth.

Design

A systematic literature review guided by PRISMA 2020 guidelines.

Methods

A comprehensive search across seven databases (PubMed, EBSCOhost, Scopus, ScienceDirect, ProQuest, IEEE Xplore, JSTOR) from 2015 to 2025 identified 14 eligible studies (7 RCTs, 7 Quasi-experiments) which were synthesized across East Asia, Europe and Africa. Quality was appraised using JBI tools, with the protocol registered in PROSPERO.

Results

Synthesis of 14 studies (n = 1107) reveals a critical divergence. AI interventions significantly improved psychomotor application, procedural skills and communication mechanics compared with traditional instructor-led teaching methods. However, findings on HOTS and retention were mixed; while GenAI enhanced inquiry-based problem solving, it occasionally compromised critical reflection and long-term knowledge retention compared with human-led instruction. Furthermore, a "gap in algorithmic empathy" was evident, with AI failing to effectively enhance cultural awareness.

Conclusion

AI functions as a robust "pedagogical scaffold" for procedural simulation but risks inducing "cognitive offloading" if used passively. Educators must adopt a Hybrid-Scaffolded Model: leveraging AI for iterative drills while prioritizing human facilitation for deep cognitive consolidation, ethics and cultural safety.

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护理教育中的生成式人工智能和智能辅导系统：对高阶思维技能和临床能力影响的系统回顾

目的综合人工智能（AI）对护理学生临床能力、高阶思维技能（HOTS）和教育成果的影响，以区分程序效率和深度认知保留。生成式人工智能和智能辅导系统（ITS）提供个性化的脚手架。然而，它们与传统教学方法（如面对面讲座和标准的非人工智能模拟）的比较效果仍然存在争议，特别是在技能习得和认知深度之间的潜在权衡方面。设计以PRISMA 2020指南为指导的系统文献综述。方法综合检索2015 - 2025年在东亚、欧洲和非洲地区合成的PubMed、EBSCOhost、Scopus、ScienceDirect、ProQuest、IEEE Xplore、JSTOR等7个数据库，筛选出14项符合条件的研究（7项rct， 7项准实验）。使用JBI工具评估质量，并在PROSPERO中注册方案。结果综合14项研究（n = 1107）发现了一个关键的分歧。与传统的教师主导的教学方法相比，人工智能干预显著提高了精神运动应用、程序技能和沟通机制。然而，关于HOTS和保留率的调查结果却参差不齐；虽然GenAI增强了基于探究的问题解决能力，但与人类主导的教学相比，它偶尔会损害批判性反思和长期知识保留。此外，“算法同理心的差距”很明显，人工智能未能有效提高文化意识。人工智能作为程序模拟的强大“教学支架”，但如果被动使用，可能会导致“认知卸载”。教育工作者必须采用混合脚手架模型：利用人工智能进行迭代练习，同时优先考虑人类促进深度认知巩固、道德和文化安全。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nurse Education in Practice NURSING-

CiteScore

5.40

自引率

9.40%

发文量

180

审稿时长

51 days

期刊介绍： Nurse Education in Practice enables lecturers and practitioners to both share and disseminate evidence that demonstrates the actual practice of education as it is experienced in the realities of their respective work environments. It is supportive of new authors and will be at the forefront in publishing individual and collaborative papers that demonstrate the link between education and practice.