Evidence map›Paper›PMID 40896350›Full record

ArticleIntegrative medicine research2025

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

Seungwon Shin, Jihyun Sang, Ji-Sun You

Abstract read
In one paragraph

Article in Integrative medicine research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Seungwon ShinCollege of Korean Medicine, Sangji University, Wonju, Republic of Korea.
Jihyun SangCollege of Korean Medicine, Sangji University, Wonju, Republic of Korea.
Ji-Sun YouCollege of Korean Medicine, Sangji University, Wonju, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Education, MedicalGenerative Artificial IntelligenceMedicine, Korean TraditionalSurveys and Questionnaires

Identifiers

PMID40896350
PMCPMC12395378

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.