Timo Kovalainen , Sari Pramila-Savukoski , Heli-Maria Kuivila , Jonna Juntunen , Erika Jarva , Matias Rasi , Kristina Mikkonen
{"title":"Utilising artificial intelligence in developing education of health sciences higher education: An umbrella review of reviews","authors":"Timo Kovalainen , Sari Pramila-Savukoski , Heli-Maria Kuivila , Jonna Juntunen , Erika Jarva , Matias Rasi , Kristina Mikkonen","doi":"10.1016/j.nedt.2025.106600","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>This umbrella review of reviews aims to synthesise current evidence on AIʼs utilisation in developing education within health sciences disciplines.</div></div><div><h3>Design</h3><div>An umbrella review of reviews, review of reviews, based on Joanna Briggs Institute guidelines.</div></div><div><h3>Data selection</h3><div>CINAHL, ERIC(ProQuest), PubMed, Scopus, and Medic were systematically searched in December 2023 with no time limit. The inclusion and exclusion criteria were defined according to the PCC framework: Participants(P), Concept(C), and Context (C). Two independent researchers screened 6304 publications, and 201 reviews were selected in the full-text phase.</div></div><div><h3>Data extraction</h3><div>All the reviews that met inclusion criteria were included in the analysis. The reference lists of included reviews were also searched. Included reviews were quality appraised. The results were analysed with narrative synthesis.</div></div><div><h3>Results of data synthesis</h3><div>Seven reviews published between 2019 and 2023 were selected for analysis. Five key domains were identified: robotics, machine learning and deep learning, big data, immersive technologies, and natural language processing. Robotics enhances practical medical, dental and nursing education training. Machine learning personalises learning experiences and improves diagnostic skills. Immersive technologies provide interactive simulations for practical training.</div></div><div><h3>Conclusion</h3><div>This umbrella review of reviews highlights the potential of AI in health sciences education and the need for continued investment in AI technologies and ethical frameworks to ensure effective and equitable integration into educational practices.</div></div>","PeriodicalId":54704,"journal":{"name":"Nurse Education Today","volume":"147 ","pages":"Article 106600"},"PeriodicalIF":3.6000,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nurse Education Today","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0260691725000358","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
引用次数: 0
Abstract
Objective
This umbrella review of reviews aims to synthesise current evidence on AIʼs utilisation in developing education within health sciences disciplines.
Design
An umbrella review of reviews, review of reviews, based on Joanna Briggs Institute guidelines.
Data selection
CINAHL, ERIC(ProQuest), PubMed, Scopus, and Medic were systematically searched in December 2023 with no time limit. The inclusion and exclusion criteria were defined according to the PCC framework: Participants(P), Concept(C), and Context (C). Two independent researchers screened 6304 publications, and 201 reviews were selected in the full-text phase.
Data extraction
All the reviews that met inclusion criteria were included in the analysis. The reference lists of included reviews were also searched. Included reviews were quality appraised. The results were analysed with narrative synthesis.
Results of data synthesis
Seven reviews published between 2019 and 2023 were selected for analysis. Five key domains were identified: robotics, machine learning and deep learning, big data, immersive technologies, and natural language processing. Robotics enhances practical medical, dental and nursing education training. Machine learning personalises learning experiences and improves diagnostic skills. Immersive technologies provide interactive simulations for practical training.
Conclusion
This umbrella review of reviews highlights the potential of AI in health sciences education and the need for continued investment in AI technologies and ethical frameworks to ensure effective and equitable integration into educational practices.
期刊介绍:
Nurse Education Today is the leading international journal providing a forum for the publication of high quality original research, review and debate in the discussion of nursing, midwifery and interprofessional health care education, publishing papers which contribute to the advancement of educational theory and pedagogy that support the evidence-based practice for educationalists worldwide. The journal stimulates and values critical scholarly debate on issues that have strategic relevance for leaders of health care education.
The journal publishes the highest quality scholarly contributions reflecting the diversity of people, health and education systems worldwide, by publishing research that employs rigorous methodology as well as by publishing papers that highlight the theoretical underpinnings of education and systems globally. The journal will publish papers that show depth, rigour, originality and high standards of presentation, in particular, work that is original, analytical and constructively critical of both previous work and current initiatives.
Authors are invited to submit original research, systematic and scholarly reviews, and critical papers which will stimulate debate on research, policy, theory or philosophy of nursing and related health care education, and which will meet and develop the journal''s high academic and ethical standards.