Implementing Artificial Intelligence in Physiotherapy Education: A Case Study on the Use of Large Language Models (LLM) to Enhance Feedback

IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ignacio Villagrán;Rocío Hernández;Gregory Schuit;Andrés Neyem;Javiera Fuentes-Cimma;Constanza Miranda;Isabel Hilliger;Valentina Durán;Gabriel Escalona;Julián Varas
{"title":"Implementing Artificial Intelligence in Physiotherapy Education: A Case Study on the Use of Large Language Models (LLM) to Enhance Feedback","authors":"Ignacio Villagrán;Rocío Hernández;Gregory Schuit;Andrés Neyem;Javiera Fuentes-Cimma;Constanza Miranda;Isabel Hilliger;Valentina Durán;Gabriel Escalona;Julián Varas","doi":"10.1109/TLT.2024.3450210","DOIUrl":null,"url":null,"abstract":"This article presents a controlled case study focused on implementing and using generative artificial intelligence, specifically large language models (LLMs), in physiotherapy education to assist instructors with formulating effective technology-mediated feedback for students. It outlines how these advanced technologies have been integrated into an existing feedback-oriented platform to guide instructors in providing feedback inputs and establish a reference framework for future innovations in practical skills training for health professions education. Specifically, the proposed solution uses LLMs to automatically evaluate feedback inputs made by instructors based on predefined and literature-based quality criteria and generates actionable textual explanations for reformulation. In addition, if the instructor requires, the tool supports summary generation for large sets of text inputs to achieve better student reception and understanding. The case study describes how these features were integrated into the feedback-oriented platform, how their effectiveness was evaluated in a controlled setting with documented feedback inputs, and the results of its implementation with real users through cognitive walkthroughs. Initial results indicate that this innovative implementation holds great potential to enhance learning and performance in physiotherapy education and has the potential to expand to other health disciplines where the development of procedural skills is critical, offering a valuable tool to assess and improve feedback based on quality standards for effective feedback processes. The cognitive walkthroughs allowed us to determine participants' usability decisions in the face of these new features and to evaluate the perceived usefulness, how this would integrate into their workload, and their opinion regarding the potential for the future within this teaching strategy. This article concludes with a discussion of the implications of these findings for practice and future research directions in this developing field.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"2079-2090"},"PeriodicalIF":2.9000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Learning Technologies","FirstCategoryId":"95","ListUrlMain":"https://ieeexplore.ieee.org/document/10648793/","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

This article presents a controlled case study focused on implementing and using generative artificial intelligence, specifically large language models (LLMs), in physiotherapy education to assist instructors with formulating effective technology-mediated feedback for students. It outlines how these advanced technologies have been integrated into an existing feedback-oriented platform to guide instructors in providing feedback inputs and establish a reference framework for future innovations in practical skills training for health professions education. Specifically, the proposed solution uses LLMs to automatically evaluate feedback inputs made by instructors based on predefined and literature-based quality criteria and generates actionable textual explanations for reformulation. In addition, if the instructor requires, the tool supports summary generation for large sets of text inputs to achieve better student reception and understanding. The case study describes how these features were integrated into the feedback-oriented platform, how their effectiveness was evaluated in a controlled setting with documented feedback inputs, and the results of its implementation with real users through cognitive walkthroughs. Initial results indicate that this innovative implementation holds great potential to enhance learning and performance in physiotherapy education and has the potential to expand to other health disciplines where the development of procedural skills is critical, offering a valuable tool to assess and improve feedback based on quality standards for effective feedback processes. The cognitive walkthroughs allowed us to determine participants' usability decisions in the face of these new features and to evaluate the perceived usefulness, how this would integrate into their workload, and their opinion regarding the potential for the future within this teaching strategy. This article concludes with a discussion of the implications of these findings for practice and future research directions in this developing field.
在物理治疗教育中实施人工智能:使用大型语言模型(LLM)加强反馈的案例研究
本文介绍了一项受控案例研究,重点是在物理治疗教育中实施和使用生成式人工智能,特别是大型语言模型(LLMs),以协助教师为学生制定有效的技术中介反馈。该研究概述了如何将这些先进技术集成到现有的反馈导向平台中,以指导教师提供反馈输入,并为未来卫生专业教育实践技能培训的创新建立参考框架。具体来说,所提出的解决方案使用 LLMs,根据预定义和基于文献的质量标准自动评估指导教师的反馈输入,并生成可操作的文本解释,以便重新表述。此外,如果指导教师需要,该工具还支持生成大量文本输入的摘要,以便学生更好地接收和理解。本案例研究介绍了如何将这些功能集成到以反馈为导向的平台中,如何在受控环境下通过记录反馈输入评估其有效性,以及通过认知演练在真实用户中的实施结果。初步结果表明,这种创新的实施方式在提高物理治疗教育的学习和绩效方面具有巨大的潜力,并有可能扩展到对程序技能的发展至关重要的其他健康学科,为根据有效反馈过程的质量标准评估和改进反馈提供了宝贵的工具。通过认知演练,我们确定了参与者在面对这些新功能时的可用性决定,并评估了他们所感受到的实用性、如何将其融入他们的工作量,以及他们对这一教学策略未来潜力的看法。本文最后讨论了这些发现对这一发展中领域的实践和未来研究方向的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE Transactions on Learning Technologies
IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
7.50
自引率
5.40%
发文量
82
审稿时长
>12 weeks
期刊介绍: The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信