Medical students' perceptions of AI-based feedback and feedforward on communication skills in doctor-patient consultation - an acceptance study in a video-based simulation.

IF 4.4 2区 医学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Medical Education Online Pub Date : 2025-12-31 Epub Date: 2025-12-01 DOI:10.1080/10872981.2025.2592414
Moritz Bauermann, Thomas Rotthoff, Tobias Hallmen, Miriam Kunz, Elisabeth André, Ann-Kathrin Schindler
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Abstract

Feedback and feedforward are highly relevant in promoting students' learning. With advances in artificial intelligence (AI), new opportunities to support feedback and feedforward are emerging. However, few studies have explored how medical students perceive and accept AI-based feedback and feedforward in medical communication training. In this study, we explored medical students' perceptions of AI-based and avatar-mediated feedback and feedforward in a simulation applying a doctor-patient consultation video. The participants comprised 82 medical students (56.1% female), 66 of whom were in their second semester and 16 in their fourth semester. Before participants saw a video of a medical student in a standardized pre-recorded consultation, they were asked to put themselves in the position of the peer shown. A human-like avatar subsequently provided AI-based feedback and feedforward for the medical student in the video. The participants-still taking on the shown medical student's role-were then asked to rate the perceived trustworthiness and their potential learning acceptance of the AI-based feedback and feedforward. The participants' ratings of trustworthiness and potential learning acceptance were higher for the AI-based, avatar-mediated feedforward than the feedback. Additionally, they reported a generally positive attitude toward AI. This attitude was positively correlated with a higher potential learning acceptance of feedback. The tendency to favor feedforward over feedback in interpersonal contexts-as described in the literature-was evident for the perception of the AI-based, avatar-mediated evaluations of a simulated doctor-patient consultation video. Future research could apply these insights to enhance AI-based learning in medical education, e.g. by providing students with AI-based feedforward on their own consultation videos and assessing their perceptions of the same.

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医学生对医患咨询中基于人工智能的反馈和前馈沟通技巧的看法——基于视频模拟的接受度研究
反馈和前馈在促进学生学习方面是高度相关的。随着人工智能(AI)的进步,支持反馈和前馈的新机会正在出现。然而,很少有研究探讨医学生在医学沟通训练中如何感知和接受基于ai的反馈和前馈。在这项研究中,我们探讨了医学生对基于人工智能和avatar介导的反馈和前馈的感知,并应用了一个模拟的医患咨询视频。参与者包括82名医学院学生(56.1%为女性),其中66名是第二学期的学生,16名是第四学期的学生。在参与者观看一名医学生在标准化的预先录制的咨询视频之前,他们被要求把自己放在所显示的同龄人的位置上。随后,一个人形化身为视频中的医学生提供了基于人工智能的反馈和前馈。参与者仍然扮演所示医学院学生的角色,然后被要求对感知的可信度和他们对基于人工智能的反馈和前馈的潜在学习接受度进行评分。参与者的可信度和潜在学习接受度评分在基于人工智能的、虚拟形象介导的前馈中高于反馈。此外,他们普遍对人工智能持积极态度。这种态度与更高的潜在学习接受反馈呈正相关。正如文献中所描述的那样,在人际环境中倾向于前馈而不是反馈的趋势在基于人工智能的感知中是明显的,虚拟化身介导的模拟医患咨询视频的评估。未来的研究可以应用这些见解来增强医学教育中基于人工智能的学习,例如,为学生提供基于人工智能的咨询视频反馈,并评估他们对这些视频的看法。
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来源期刊
Medical Education Online
Medical Education Online EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
6.00
自引率
2.20%
发文量
97
审稿时长
8 weeks
期刊介绍: Medical Education Online is an open access journal of health care education, publishing peer-reviewed research, perspectives, reviews, and early documentation of new ideas and trends. Medical Education Online aims to disseminate information on the education and training of physicians and other health care professionals. Manuscripts may address any aspect of health care education and training, including, but not limited to: -Basic science education -Clinical science education -Residency education -Learning theory -Problem-based learning (PBL) -Curriculum development -Research design and statistics -Measurement and evaluation -Faculty development -Informatics/web
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