Effects of artificial intelligence based physiotherapy educational approach in developing clinical reasoning skills: a randomized controlled trial.

IF 3.2 2区 医学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Gizem Ergezen Sahin, Gulay Aras Bayram, Alberto Sanchez Sierra, Simay Akdemir, Dogukan Kurc, Devrim Tarakci, Ayse Nur Tunali
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引用次数: 0

Abstract

Background: Artificial intelligence (AI) tools such as ChatGPT are increasingly being integrated into health professions education, but evidence regarding their application in physiotherapy remains limited. This study aims to investigate the impact of AI-assisted problem-based learning (AI-PBL) on theoretical knowledge, clinical competence, AI self-efficacy, internet addiction, and reading motivation compared with traditional PBL.

Methods: A randomized controlled trial was conducted with undergraduate physiotherapy students assigned to AI-PBL or PBL groups. Participants completed assessments before, immediately after, and two weeks after the group intervention. Outcome measures included a theoretical knowledge test, the Mini Clinical Evaluation Exercise (Mini-CEX), the AI ​​Self-Efficacy Scale (AI-SES), the Internet Addiction Test (IAT), and the Adult Reading Motivation Scale (ARMS).

Results: Forty students were randomized equally into two groups: AI-PBL (n = 20) and traditional PBL (n = 20). Both groups showed significant improvements in knowledge and reading motivation. The AI-PBL group showed significantly greater improvement in knowledge retention at 2 weeks (Cohen's d = 3.14) and greater gains in AI self-efficacy. Although Mini-CEX scores were higher in the AI-PBL group, the differences between groups were not statistically significant. No significant increase in internet addiction was observed in the AI-PBL group.

Conclusion: These findings emphasize that supervised, structured use of generative AI in education can enhance sustained learning and digital self-efficacy without posing behavioral risks. The AI-PBL approach appears to foster active reflection, self-directed learning, and deeper academic engagement offering a promising direction for digital innovation in physiotherapy education. Future studies should explore long-term outcomes, track behavioral engagement, and further validate the benefits of AI-enhanced instructional strategies.

Trial registration: Prior to the initiation of the study, the protocol was registered on https//www.

Clinicaltrials: gov/, and registration status was made publicly available (Identifier NCT07010991 Date 08.06.2025). ( https://clinicaltrials.gov/study/NCT07010991?term=NCT07010991&rank=1 ).

基于人工智能的物理治疗教育方法对临床推理技能培养的影响:一项随机对照试验。
背景:ChatGPT等人工智能(AI)工具正越来越多地融入卫生专业教育,但有关其在物理治疗中的应用的证据仍然有限。本研究旨在探讨人工智能辅助问题基础学习(AI-PBL)与传统PBL相比,对理论知识、临床能力、人工智能自我效能感、网络成瘾和阅读动机的影响。方法:采用随机对照试验,将物理治疗本科学生分为AI-PBL组和PBL组。参与者在小组干预之前、之后和两周后完成了评估。结果测量包括理论知识测试、迷你临床评估练习(Mini- cex)、人工智能自我效能量表(AI- ses)、网络成瘾测试(IAT)和成人阅读动机量表(ARMS)。结果:40名学生随机分为AI-PBL组(n = 20)和传统PBL组(n = 20)。两组在知识和阅读动机方面都有显著提高。AI- pbl组在2周时的知识保留率有显著提高(Cohen’s d = 3.14), AI自我效能感也有显著提高。虽然AI-PBL组Mini-CEX评分较高,但组间差异无统计学意义。AI-PBL组没有观察到网络成瘾的显著增加。结论:这些发现强调,在教育中监督、结构化地使用生成式人工智能可以增强持续学习和数字自我效能,而不会带来行为风险。AI-PBL方法似乎促进了积极反思、自主学习和更深入的学术参与,为物理治疗教育的数字化创新提供了一个有希望的方向。未来的研究应该探索长期结果,跟踪行为参与,并进一步验证人工智能增强教学策略的好处。试验注册:在研究开始之前,在https//www.Clinicaltrials: gov/上注册该方案,并公开注册状态(标识符NCT07010991日期08.06.2025)。(https://clinicaltrials.gov/study/NCT07010991?term=NCT07010991&rank=1)。
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来源期刊
BMC Medical Education
BMC Medical Education EDUCATION, SCIENTIFIC DISCIPLINES-
CiteScore
4.90
自引率
11.10%
发文量
795
审稿时长
6 months
期刊介绍: BMC Medical Education is an open access journal publishing original peer-reviewed research articles in relation to the training of healthcare professionals, including undergraduate, postgraduate, and continuing education. The journal has a special focus on curriculum development, evaluations of performance, assessment of training needs and evidence-based medicine.
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