Lindya Okti Herbawani, Ari Indra Susanti, Qorinah Estiningtyas Sakilah Adnani
{"title":"The Revolution in Midwifery Education: How AI and Deep Learning are Transforming Outcome-Based Assessments?","authors":"Lindya Okti Herbawani, Ari Indra Susanti, Qorinah Estiningtyas Sakilah Adnani","doi":"10.2147/AMEP.S543098","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Currently, midwifery education is confronted with a variety of obstacles, such as inadequate resources and conventional learning methods that are less effective in enhancing the clinical skills of students. Technological advancements and the rapid evolution of maternal and neonatal health services necessitate the transformation of midwifery education to a competency-based curriculum and outcome-based assessment paradigm. Artificial intelligence (AI) and deep learning have the potential to provide adaptive, personalized, and precise learning in this context. Nevertheless, its implementation continues to encounter a variety of challenges.</p><p><strong>Purpose: </strong>This study reviews the role of AI and deep learning algorithms in enhancing outcome-based assessments in midwifery education, focusing on improvements in objectivity, personalized learning, and students' clinical readiness.</p><p><strong>Patients and methods: </strong>This study employed a systematic literature review from Science Direct, Semantic Scholar, Springer Nature, and Taylor and Francis databases. Rayyan's software was employed to select 15 articles from the 771 articles that were discovered, in accordance with the inclusion and exclusion criteria. To guarantee objectivity and quality, two researchers conducted an independent evaluation.</p><p><strong>Results: </strong>Our review indicates that algorithms including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Random Forest, and Support Vector Machine (SVM) are proficient in facilitating objective evaluations, delivering tailored feedback, and enhancing clinical learning simulations. Artificial intelligence has demonstrated the capacity to enhance students' communication, critical thinking, and clinical decision-making abilities. The primary challenges encompass infrastructure preparedness, digital literacy, and ethical concerns pertaining to data protection and algorithmic prejudice.</p><p><strong>Conclusion: </strong>Artificial intelligence and deep learning possess significant promise to revolutionize achievement-based assessments in midwifery education through accurate, adaptable, and scalable evaluations. The successful implementation relies on the management of technological, pedagogical, and ethical restrictions, along with thorough integration into the curriculum to equip graduates for global maternal and neonatal health concerns.</p>","PeriodicalId":47404,"journal":{"name":"Advances in Medical Education and Practice","volume":"16 ","pages":"1579-1599"},"PeriodicalIF":1.7000,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12409338/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Medical Education and Practice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2147/AMEP.S543098","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Currently, midwifery education is confronted with a variety of obstacles, such as inadequate resources and conventional learning methods that are less effective in enhancing the clinical skills of students. Technological advancements and the rapid evolution of maternal and neonatal health services necessitate the transformation of midwifery education to a competency-based curriculum and outcome-based assessment paradigm. Artificial intelligence (AI) and deep learning have the potential to provide adaptive, personalized, and precise learning in this context. Nevertheless, its implementation continues to encounter a variety of challenges.
Purpose: This study reviews the role of AI and deep learning algorithms in enhancing outcome-based assessments in midwifery education, focusing on improvements in objectivity, personalized learning, and students' clinical readiness.
Patients and methods: This study employed a systematic literature review from Science Direct, Semantic Scholar, Springer Nature, and Taylor and Francis databases. Rayyan's software was employed to select 15 articles from the 771 articles that were discovered, in accordance with the inclusion and exclusion criteria. To guarantee objectivity and quality, two researchers conducted an independent evaluation.
Results: Our review indicates that algorithms including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Random Forest, and Support Vector Machine (SVM) are proficient in facilitating objective evaluations, delivering tailored feedback, and enhancing clinical learning simulations. Artificial intelligence has demonstrated the capacity to enhance students' communication, critical thinking, and clinical decision-making abilities. The primary challenges encompass infrastructure preparedness, digital literacy, and ethical concerns pertaining to data protection and algorithmic prejudice.
Conclusion: Artificial intelligence and deep learning possess significant promise to revolutionize achievement-based assessments in midwifery education through accurate, adaptable, and scalable evaluations. The successful implementation relies on the management of technological, pedagogical, and ethical restrictions, along with thorough integration into the curriculum to equip graduates for global maternal and neonatal health concerns.
背景:目前,助产学教育面临着各种障碍,如资源不足,传统的学习方法在提高学生临床技能方面效果不佳。技术进步和孕产妇和新生儿保健服务的迅速发展要求将助产教育转变为以能力为基础的课程和以结果为基础的评估模式。在这种情况下,人工智能(AI)和深度学习有可能提供自适应、个性化和精确的学习。然而,它的执行继续遇到各种各样的挑战。目的:本研究回顾了人工智能和深度学习算法在增强助产教育中基于结果的评估中的作用,重点关注客观性、个性化学习和学生临床准备的改进。患者和方法:本研究采用了来自Science Direct、Semantic Scholar、施普林格Nature和Taylor and Francis数据库的系统文献综述。根据纳入和排除标准,使用Rayyan的软件从发现的771篇文章中选择了15篇。为了保证客观性和质量,两位研究者进行了独立的评估。结果:我们的综述表明,包括卷积神经网络(CNN)、长短期记忆(LSTM)、随机森林和支持向量机(SVM)在内的算法在促进客观评估、提供量身定制的反馈和增强临床学习模拟方面非常精通。人工智能已经证明了增强学生沟通、批判性思维和临床决策能力的能力。主要挑战包括基础设施准备、数字素养以及与数据保护和算法偏见有关的道德问题。结论:人工智能和深度学习通过准确、适应性强、可扩展的评估,有望彻底改变助产教育中基于成绩的评估。成功的实施取决于对技术、教学和道德限制的管理,以及彻底融入课程,使毕业生具备全球孕产妇和新生儿健康问题的能力。