Artificial Intelligence to Support the Training and Assessment of Professionals: A Systematic Literature Review

IF 23.8 1区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

ACM Computing Surveys Pub Date : 2024-10-10 DOI:10.1145/3699712

Mariano Albaladejo-González, José A. Ruipérez-Valiente, Félix Gómez Mármol

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引用次数: 0

Abstract

Advances in Artificial Intelligence (AI) and sensors are significantly impacting multiple areas, including education and workplaces. Following the PRISMA methodology, this review explores the current status of using AI to support the training and assessment of professionals. We examined 83 research papers, analyzing: (1) the targeted professionals, (2) the skills assessed, (3) the AI algorithms utilized, (4) the data and devices employed, (5) data fusion techniques utilized, (6) the architecture of the proposed platforms, (7) the management of ethics and privacy, and (8) validations of the proposals. The review highlights a trend in evaluating healthcare professionals (especially surgeons) motivated by the critical role of hands-on training in these professionals. Besides, the review reveals that data fusion techniques and certain technologies, like transfer learning and explainable AI, are not widely utilized despite their huge potential. Finally, the review underscores that most proposals remain within the research domain, lacking the integration and maturity needed for sustained use in real-world environments. Therefore, most of the proposals are not currently available to support the training of professionals. The insights of this review can guide researchers aiming to improve the training of professionals and, consequently, their education.

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人工智能支持专业人员的培训和评估：系统性文献综述

人工智能（AI）和传感器的进步正在对包括教育和工作场所在内的多个领域产生重大影响。按照 PRISMA 方法，本综述探讨了使用人工智能支持专业人员培训和评估的现状。我们研究了 83 篇研究论文，分析了：(1) 目标专业人员；(2) 评估的技能；(3) 使用的人工智能算法；(4) 使用的数据和设备；(5) 使用的数据融合技术；(6) 拟议平台的架构；(7) 道德和隐私管理；(8) 建议的验证。综述强调了对医疗保健专业人员（尤其是外科医生）进行评估的趋势，这是因为实践培训对这些专业人员起着至关重要的作用。此外，综述还显示，数据融合技术和某些技术（如迁移学习和可解释人工智能）尽管潜力巨大，但并未得到广泛应用。最后，审查强调，大多数建议仍停留在研究领域，缺乏在现实世界环境中持续使用所需的整合和成熟度。因此，大多数建议目前还不能用于支持专业人员的培训。本综述的见解可以指导研究人员改进专业人员的培训，进而改进他们的教育。

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

ACM Computing Surveys 工程技术-计算机：理论方法

CiteScore

33.20

自引率

0.60%

发文量

372

审稿时长

12 months

期刊介绍： ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.