护理普及教育中的人工智能和预测分析

and then a smaller sub-set as a test dataset to forecast student attrition from the programme. They reported this method achieved 84% sensitivity, 70% specificity, and 94% overall accuracy. However, it was several years later before AI was employed again in pedagogical nursing research. A recent empirical study developed a predictive model to gauge if a nursing student would graduate from their university education programme at different points in their professional training (Hannaford et al., 2021). The researchers used eight different machine learning algorithms, i.e., random forest, xgboost, neural networks, support vector machine, C5.0, Naïve Bayes, K-nearest neighbour, and logistic regression, to build a model and compare the accuracy of the various techniques in determining nursing student graduation.

While most research studies focus on developing and testing AI algorithms and their associated predictive models, Swan (2021) administered an online survey (n = 675) with nursing students, nurse faculty, and practising nurses in the United States to explore their level of knowledge, use of, and attitudes towards AI in healthcare. Only 30% reported knowing how AI was used in clinical nursing practice and the majority had only fair or no understanding of technologies used in AI, with some participants highlighting nurses need a range of competencies in this area. Other studies have gone further and implemented an AI based tool in the real-world of nursing. For instance, Narang et al. (2021) compared nurses using an echocardiograph enhanced via a deep learning algorithm to sonographers using a standard echocardiograph, to understand if this type of AI approach could produce the same quality diagnostic medical images with limited training and clinical experience. The authors noted this could be particularly beneficial for both education and clinical practice in low resource settings. A study protocol by Shorey et al. (2019) also described a virtual counselling chatbot that would run on a NLP based system. In theory, this would interact with nursing students as a virtual patient to try and improve their communication skills.

Some nursing researchers have begun to synthesise literature on AI in nursing education. Harmon et al. (2021) undertook a scoping review of AI and virtual reality in clinical simulation for nursing pain education and found only four relevant studies. However, the deion of AI used in this review seems to loosely centre on ‘computer assisted instruction’. Furthermore, none of the four included studies mention any specific AI technique in the development or application of the simulation technology, nor are any actual AI approaches noted in the review results. This may indicate a lack of understanding or confusion about what this field encompasses. A recent review of the literature on AI in nursing more broadly included over 100 studies (O’Connor et al., 2021), but highlighted the limited number related to nursing education, as most of the current evidence base focuses on clinical nursing practice in hospital settings. The review also emphasised the need to train and upskill nurses in AI, which others have also noted to prepare students, faculty, and nurses in practice for digitally enabled healthcare now and into the future (Booth et al., 2021, O'Connor and LaRue, 2021).

An international nursing consortium on AI convened in 2019, to identify priority areas for action (Ronquillo et al., 2021). They highlighted a lack of nurse faculty expertise in health informatics and AI that needs to be urgently addressed, so this topic can be taught in undergraduate and postgraduate nursing programmes. They posit that a lack of knowledge of AI, among other issues, could hold back the profession from participating in and leading AI initiatives in healthcare. Drawing on the experiences of other professional colleagues who are starting to utilise AI more in teaching and learning could benefit nursing. Masters (2019) discussed the future of AI in medical education, describing a number of potentially useful applications such as ‘intelligent’ systems that can respond to gaps in student knowledge and provide personalised feedback, virtual facilitators that could support students at university and during clinical training, and algorithms for administrative tasks such as tracking engagement and attendance. A professor at Georgia Institute of Technology deployed a virtual teaching assistant, based on the IBM Watson platform, for an online Masters programme to provide feedback and support to computer science students posting on online forums, which the graduate students seemed to value (Georgia Institute of Technology, 2016). Predictive learning analytics are also becoming popular where past student data is being mined using machine learning techniques to predict how current and future students may behave. These insights could guide university recruitment, course enrolment, and pedagogical strategies to customise learning, assessment, and feedback, as well as identify at-risk students, although Ekowo and Palmer (2016) advise these analytical tools should be used ethically so as not to disadvantage certain student groups.

However, Bayne (2015) argues that automated teaching tools such as virtual teacherbots seem to be driven by “productivity-oriented solutionism”, when pedagogy grounded in humanism should form the foundation of higher education. Popenici and Kerr (2017) also discusses AI more broadly in higher education, emphasising it is more likely these sophisticated computational approaches will extend the capabilities of educators and enhance the learning process and environment rather than replace teaching staff. Nevertheless, they advocate for transparency and oversight in how AI is applied across the university sector, to help reduce the risk of algorithmic bias. This can come from unrepresentative datasets and the corresponding algorithms, predictive modelling, and decision making derived from these, which could exacerbate structural inequalities in higher education. For example, the University of Texas Austin had been using a machine learning system for several years to assess applications to a computer science doctoral programme as it saved faculty administrative time. However, the system was stopped in 2020 because the algorithms were trained using a database of past admission decisions and some argued that historical inequity could introduce racial, gender, and other biases into the process (Burke, 2020).

It remains to be seen if AI will be embraced in nursing education, by faculty, students, university management, and administration. What is clear is that if these computational techniques are going to be applied, then great care needs to be taken to ensure they help us predict a better future in teaching and learning for both students and educators and do not become an unhealthy predilection concerned solely with saving money and time. No doubt AI will continue to evolve and advance, meaning there could be some merit in applying it wisely in nursing education.

全文翻译（列出）

智能化 (AI) 其实是低收入和高等普及教育层面的取而代之风靡语。它喻为妥善解决不良影响护理一号机械工程执法人员、病变、同学和普及教育工译者的疑虑的一种步骤，它试图更是高预报身心健康、深造、维修服务和其他结果并因此特别强调各项政策的加速和真实性。虽然它像是是一个相对较取而代之的技术开发其发展，但智能化的起源地可以始自 1950 年代，并且在依然几年中会年中了连续的其发展和解体取而代之一轮（Boden，2018 年）。它的创始人之一将智能化定义为“工业用智能化一号机器的科学和工程，尤其是智能化计算一号机程序”（McCarthy，2007）。这些计算步骤在 1990 年代开始受到关注，当时一号机器深造通过一系列监理深造技术开发挑选出。这使得并不能够相结合更是较为简单的统计仿真，这些仿真并不能够特别强调硬质的、期望值性的各项政策。例如，在美国，Harvey (1993)可用人工数据分析来背书眼科执法人员围绕检验特别强调的各项政策。由于语言学和统计学的其发展以及计算能力的更是高，智能化的另一个分支重构处理 (NLP) 在这个时期也开始蓬勃其发展 ( Manning, 1999)。尽管智能化取得了飞跃，但随着无监理深造和强化深造技术开发的出现，使“剖面深造”视为显然' 以及 NLP 系统会的飞跃，许多年后智能化才系统设计于眼科普及教育。

2008 年，Moseley 和 Mead (2008)可用一种称为各项政策树的一号机器深造技术开发来预报英国本科眼科文凭的辍学率。该演算法可用相对小得多的 3978 名的大同学记录信息集，来自 528 名眼科同学，将其用作训练信息集，然后将小得多的子集用作测试信息集，以预报该计划的同学过多。他们调查结果说这种步骤解决问题了 84% 的孔径、70% 的特异性和 94% 的总体准确度。然而，几年后，智能化再次被常用基础教育眼科研究成果。已经有的一项分析方法研究成果开发了一个预报仿真来衡量眼科一号机械工程同学否会在其一号机械工程培训班的各不相同阶段从的大学普及教育文凭毕业。唐纳福德等人，2021 年）。研究成果执法人员可用了八种各不相同的一号机器深造演算法，即随一号机森林、xgboost、数据分析、背书formula_一号机、C5.0、简练贝叶斯、K-已经有西南边和逻辑回归，来相结合仿真并较为各种演算法的真实性。断定眼科同学毕业的技术开发。

虽然大多数研究成果侧重于开发和测试智能化演算法及其系统会性的预报仿真，但Swan (2021)对美国的眼科一号机械工程同学、眼科执法人员教职员和执业眼科执法人员顺利进行了一项网络调查 (n = 675)，以探索他们的学问水平、在低收入中会可用智能化以及对智能化的消极态度。只有 30% 的人调查结果知道 AI 如何常用病理眼科出发点，大多数人对 AI 中会可用的技术开发只有一般的了解或不了解，一些旁观者强调眼科执法人员能够在这方面的一系列能力。其他研究成果更是进一步，在表象眼科在世界上中会实施了基于智能化的辅助工具。例如，纳朗等人。(2021)将可用通过剖面深造演算法增强的超声心动图的眼科执法人员与可用准则超声心动图的超声外科医生顺利进行了较为，以了解这种并不一定的 AI 步骤否可以在训练和病理经验可用的前提转化成相同真实性的检验医学图像。译者指出，这对资源贫乏生态环境中会的普及教育和病理出发点尤其更为重要。Shorey 等人的研究成果可行性。(2019)还描述了一个可以在基于 NLP 的系统会上运行的云端听取聊天室AI。从也就是说讲到，这将与眼科同学作为云端病变顺利进行互动，以尝试更是高他们的沟通技巧。

一些眼科研究成果执法人员仍未开始综合有关眼科普及教育中会智能化的古书。哈蒙等人。(2021)对常用眼科疼痛普及教育的病理模拟中会的智能化和云端表象顺利进行了覆盖范围审议，发现只有四项系统会性研究成果。然而，这篇评论中会对智能化的描述其实松散地集中会在“计算一号机辅助基础教育”上。此外，纳入的四项研究成果均未明确指出模拟技术开发开发或系统设计中会的任何特定 AI 技术开发，审议结果中会也未明确指出任何单单的 AI 步骤。这显然指出对该层面所涵盖的概要考虑到了解或重名。已经有对眼科智能化古书的回顾更是为广泛地包括了 100 多项研究成果（O'Connor 等人，2021 年）)，但强调与眼科普及教育系统会性的生产量可用，因为当前的大多数证据基础都集中会在医院生态环境中会的病理眼科出发点。该评论还强调能够培训班和更是高智能化眼科执法人员的技能，其他人也指出，这也让同学、教职员和眼科执法人员在出发点中会为现今和今后的数字化医疗做好正要（Booth 等人，2021 年，O'Connor 和拉鲁，2021 年）。

一个International智能化眼科联盟于 2019 年召开，以断定优先实际行动层面（Ronquillo 等人，2021 年）。他们强调了眼科执法人员在身心健康信息学和智能化方面的一号机械工程学问能够紧急妥善解决的疑虑，因此该意念可以在本科和研究成果生眼科文凭中会大学教授。他们视为，考虑到智能化学问等疑虑则会致使该行业积极参与和领导低收入层面的智能化计划。相结合其他开始在基础教育中会更是多地利用智能化的一号机械工程同事的经验，可以使眼科受益。学位（2019）讨论了智能化在医学普及教育中会的今后，描述了许多潜在依赖于的系统设计程序，例如可以拥护同学学问差异并提供个性化反馈的“智能化”系统会，可以在的大学和病理培训班期间为同学提供背书的云端辅导员，以及演算法常用管理护航，例如跟踪不能接受程度和出勤率。弗吉尼亚州理工学院的一位大学教授地面部队了一个基于 IBM Watson 平台的云端助教，常用网络学位文凭，为认知科学一号机械工程的同学在网络论坛上发帖提供反馈和背书，研究成果生其实很倚重这一点（弗吉尼亚州理工学院）科技, 2016）。预报深造归纳也更是为风靡，其中会可用一号机器深造技术开发挖掘依然的同学信息来预报当前和今后同学的行为。这些立论可以指导工作的大学中考、文凭注册和基础教育策略，以自定义深造、评估和反馈，以及识别有安全性的同学，尽管Ekowo 和 Palmer（2016）建言应完全符合道德地可用这些归纳辅助工具，以免使某些同学群体处于不利威信。

然而，Bayne (2015)视为，像云端教职员AI这样的数据处理基础教育辅助工具其实是由“以劳动力为出发点的妥善解决主义”动力的，而以人文主义为基础的基础教育法某种程度视为高等普及教育的基础。波佩尼奇和养父 (2017)还更是为广泛地讨论了高等普及教育中会的智能化，强调这些较为简单的计算步骤更是或许扩展普及教育工译者的能力并改善深造过程和生态环境，而不是取而代之基础教育执法人员。尽管如此，他们还是提倡对智能化在整个的大学管理工作的系统设计模式顺利进行透明和监理，以帮助增高演算法偏见的安全性。这显然来自不具权威性的信息集以及由此衍生的具体来说演算法、预报利用计算机和各项政策，这则会助长高等普及教育中会的结构性不应有。例如，密苏里州的大学林奇分校过往始终可用一号机器深造系统会来评估认知科学博士文凭的申请人，因为它节省时间了教职员的管理时间。然而，特为，2020 年）。

智能化否会被教职员、同学、的大学管理层和行政管理工作不能接受，还有待观察。很清楚的是，如果要系统设计这些计算技术开发，则能够非常进去，以尽显然它们帮助我们为同学和普及教育者预报更是高的基础教育和深造今后，而不是视为一种不身心健康的迷恋，只关注节省时间回报和时间。显然，智能化将在此之后其发展和飞跃，这意味着在眼科普及教育中会明智地系统设计它则会有一些好处。

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