{"title":"Enhancing learning outcomes through AI-driven simulation in nursing education: A systematic review","authors":"Tuba Sengul RN, PhD, CWON, Seda Sarıköse RN, PhD","doi":"10.1016/j.ecns.2025.101797","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Artificial intelligence (AI) is increasingly integrated into simulation-based nursing education to enhance scalability, personalisation, and interactivity. This review systematically examined the impact of AI-driven simulations on learning outcomes in nursing education.</div></div><div><h3>Method</h3><div>This systematic review adhered to PRISMA guidelines and included empirical studies published between 2015 and 2025. A comprehensive search was conducted across six databases, and study quality was appraised using RoB 2, ROBINS-I, JBI, and MMAT tools. Methodological heterogeneity precluded meta-analysis; findings were instead synthesised through deductive categorisation to ensure a structured and critical integration.</div></div><div><h3>Results</h3><div>Sixteen studies met the inclusion criteria. AI-driven simulations were associated with improved communication, clinical reasoning, knowledge acquisition, self-efficacy, and empathy. Most studies reported high levels of learner satisfaction and engagement. However, limitations included challenges in interpreting nuanced emotional cues, limited cultural adaptability of AI systems, and technological constraints affecting responsiveness.</div></div><div><h3>Conclusions</h3><div>AI-driven simulation supports the development of diverse learning outcomes in nursing education. Further research is needed to explore long-term effects and optimise implementation strategies.</div></div>","PeriodicalId":48753,"journal":{"name":"Clinical Simulation in Nursing","volume":"106 ","pages":"Article 101797"},"PeriodicalIF":2.5000,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Simulation in Nursing","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1876139925001148","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Artificial intelligence (AI) is increasingly integrated into simulation-based nursing education to enhance scalability, personalisation, and interactivity. This review systematically examined the impact of AI-driven simulations on learning outcomes in nursing education.
Method
This systematic review adhered to PRISMA guidelines and included empirical studies published between 2015 and 2025. A comprehensive search was conducted across six databases, and study quality was appraised using RoB 2, ROBINS-I, JBI, and MMAT tools. Methodological heterogeneity precluded meta-analysis; findings were instead synthesised through deductive categorisation to ensure a structured and critical integration.
Results
Sixteen studies met the inclusion criteria. AI-driven simulations were associated with improved communication, clinical reasoning, knowledge acquisition, self-efficacy, and empathy. Most studies reported high levels of learner satisfaction and engagement. However, limitations included challenges in interpreting nuanced emotional cues, limited cultural adaptability of AI systems, and technological constraints affecting responsiveness.
Conclusions
AI-driven simulation supports the development of diverse learning outcomes in nursing education. Further research is needed to explore long-term effects and optimise implementation strategies.
期刊介绍:
Clinical Simulation in Nursing is an international, peer reviewed journal published online monthly. Clinical Simulation in Nursing is the official journal of the International Nursing Association for Clinical Simulation & Learning (INACSL) and reflects its mission to advance the science of healthcare simulation.
We will review and accept articles from other health provider disciplines, if they are determined to be of interest to our readership. The journal accepts manuscripts meeting one or more of the following criteria:
Research articles and literature reviews (e.g. systematic, scoping, umbrella, integrative, etc.) about simulation
Innovative teaching/learning strategies using simulation
Articles updating guidelines, regulations, and legislative policies that impact simulation
Leadership for simulation
Simulation operations
Clinical and academic uses of simulation.