The American Society of Radiologic Technologists (ASRT) AI educator survey: A cross-sectional study to explore knowledge, experience, and use of AI within education

IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Medical Imaging and Radiation Sciences Pub Date : 2024-07-13 DOI:10.1016/j.jmir.2024.101449
Nikolaos Stogiannos , Michael Jennings , Craig St George , John Culbertson , Hugh Salehi , Sandra Furterer , Melissa Pergola , Melissa P. Culp , Christina Malamateniou
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引用次数: 0

Abstract

Introduction

Artificial Intelligence (AI) is revolutionizing medical imaging and radiation therapy. AI-powered applications are being deployed to aid Medical Radiation Technologists (MRTs) in clinical workflows, decision-making, dose optimisation, and a wide range of other tasks. Exploring the levels of AI education provided across the United States is crucial to prepare future graduates to deliver the digital future. This study aims to assess educators’ levels of AI knowledge, the current state of AI educational provisions, the perceived challenges around AI education, and important factors for future advancements.

Methods

An online survey was electronically administered to all radiologic technologists in the American Society of Radiologic Technologists (ASRT) database who indicated that they had an educator role in the United States. This was distributed through the membership of the ASRT, from February to April 2023. All quantitative data was analysed using frequency and descriptive statistics. The survey's open-ended questions were analysed using a conceptual content analysis approach.

Results

Out of 5,066 educators in the ASRT database, 373 valid responses were received, resulting in a response rate of 7.4%. Despite 84.5% of educators expressing the importance of teaching AI, 23.7% currently included AI in academic curricula. Of the 76.3% that did not include AI in their curricula, lack of AI knowledge among educators was the top reason for not integrating AI in education (59.1%). Similarly, AI-enabled tools were utilised by only 11.1% of the programs to assist teaching. The levels of trust in AI varied among educators.

Conclusion

The study found that although US educators of MRTs have a good baseline knowledge of general concepts regarding AI, they could improve on the teaching and use of AI in their curricula. AI training and guidance, adequate time to develop educational resources, and funding and support from higher education institutions were key priorities as highlighted by educators.

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美国放射技师协会(ASRT)人工智能教育者调查:一项横向研究,旨在探索人工智能在教育领域的知识、经验和使用情况
引言 人工智能(AI)正在彻底改变医学成像和放射治疗。人工智能驱动的应用正在被部署到临床工作流程、决策、剂量优化和其他一系列任务中,以帮助医疗放射技术人员(MRT)。探索美国各地提供的人工智能教育水平对于培养未来的毕业生提供数字化未来至关重要。本研究旨在评估教育工作者的人工智能知识水平、人工智能教育的现状、人工智能教育所面临的挑战以及未来发展的重要因素。方法通过电子方式对美国放射技师学会(ASRT)数据库中所有表示自己在美国担任教育工作者的放射技师进行在线调查。该问卷于 2023 年 2 月至 4 月期间通过美国放射技师协会的会员进行发放。所有定量数据均采用频率和描述性统计进行分析。调查中的开放式问题采用概念内容分析法进行分析。结果在美国教育工作者协会数据库中的 5066 名教育工作者中,共收到 373 份有效回复,回复率为 7.4%。尽管84.5%的教育工作者表示人工智能教学非常重要,但目前有23.7%的教育工作者将人工智能纳入了学术课程。在未将人工智能纳入课程的 76.3% 中,教育工作者缺乏人工智能知识是未将人工智能纳入教育的首要原因(59.1%)。同样,只有 11.1% 的课程利用人工智能工具辅助教学。研究发现,虽然美国的 MRT 教育工作者对人工智能的一般概念有较好的基础知识,但他们可以在课程中改进人工智能的教学和使用。人工智能培训和指导、开发教育资源的充足时间以及高等教育机构的资助和支持是教育工作者强调的主要优先事项。
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来源期刊
Journal of Medical Imaging and Radiation Sciences
Journal of Medical Imaging and Radiation Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
2.30
自引率
11.10%
发文量
231
审稿时长
53 days
期刊介绍: Journal of Medical Imaging and Radiation Sciences is the official peer-reviewed journal of the Canadian Association of Medical Radiation Technologists. This journal is published four times a year and is circulated to approximately 11,000 medical radiation technologists, libraries and radiology departments throughout Canada, the United States and overseas. The Journal publishes articles on recent research, new technology and techniques, professional practices, technologists viewpoints as well as relevant book reviews.
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