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韩国传统医学教育中生成式人工智能的使用与认知:韩国本科生的横断面调查

Use and perception of generative artificial intelligence in traditional Korean medicine education: A cross-sectional survey of undergraduate students in Korea.

作者信息

Shin Seungwon, Sang Jihyun, You Ji-Sun

机构信息

College of Korean Medicine, Sangji University, Wonju, Republic of Korea.

出版信息

Integr Med Res. 2025 Dec;14(4):101216. doi: 10.1016/j.imr.2025.101216. Epub 2025 Aug 6.

DOI:10.1016/j.imr.2025.101216
PMID:40896350
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12395378/
Abstract

BACKGROUND

This study aimed to examine the usage, awareness, and satisfaction related to generative AI (GenAI) among undergraduate students at a Traditional Korean Medicine (TKM) college in Korea and to identify factors associated with GenAI use and satisfaction.

METHODS

A structured questionnaire consisting of 56 items across six domains was administered, covering demographics, general and TKM-specific GenAI use, satisfaction, educational experiences, and future expectations. Descriptive statistics, univariable analysis, multivariable logistic regression, and correlation analysis were performed.

RESULTS

A total of 234 students across six academic years participated in the survey. Most respondents were aware of GenAI (88.5 %) and used it for general purposes (79.9 %). However, only 16.2 % actively used it for TKM learning. While 70.4 % were satisfied with using GenAI in general, only 45.8 % felt satisfied with its use for TKM education. Factors significantly associated with GenAI use or satisfaction included enrollment in the TKM curriculum, older age, prior major, scholarship receipt, and self-directed GenAI learning. Although only 18.8 % had experienced GenAI in formal TKM courses, 96.2 % viewed GenAI as necessary in TKM education.

CONCLUSION

A notable gap exists between students' interest and the limited integration of GenAI in TKM curricula. To close this gap, GenAI should be systematically incorporated into educational programs, accompanied by faculty training and institutional support to enhance students' digital readiness and learning outcomes.

摘要

背景

本研究旨在调查韩国一所韩医学(TKM)学院的本科生对生成式人工智能(GenAI)的使用情况、认知程度和满意度,并确定与GenAI使用和满意度相关的因素。

方法

采用一份包含六个领域56个项目的结构化问卷,内容涵盖人口统计学、一般和韩医学特定的GenAI使用情况、满意度、教育经历及未来期望。进行了描述性统计、单变量分析、多变量逻辑回归和相关性分析。

结果

六个学年的234名学生参与了调查。大多数受访者知晓GenAI(88.5%),并将其用于一般用途(79.9%)。然而,只有16.2%的人积极将其用于韩医学学习。虽然70.4%的人总体上对使用GenAI感到满意,但只有45.8%的人对其在韩医学教育中的使用感到满意。与GenAI使用或满意度显著相关的因素包括韩医学课程注册、年龄较大、先前专业、获得奖学金以及自主进行GenAI学习。虽然只有18.8%的人在正式的韩医学课程中体验过GenAI,但96.2%的人认为GenAI在韩医学教育中是必要的。

结论

学生的兴趣与GenAI在韩医学课程中有限的整合之间存在显著差距。为了缩小这一差距,应将GenAI系统地纳入教育计划,并辅以教师培训和机构支持,以提高学生的数字素养和学习成果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c702/12395378/7fbe403106c8/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c702/12395378/ac8239a860f7/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c702/12395378/7fbe403106c8/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c702/12395378/ac8239a860f7/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c702/12395378/7fbe403106c8/gr2.jpg

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Decoding medical educators' perceptions on generative artificial intelligence in medical education.解码医学教育者对医学教育中生成式人工智能的看法。
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