Design strategies for artificial intelligence based future learning centers in medical universities.

IF 2.7 2区医学 Q1 EDUCATION & EDUCATIONAL RESEARCH

BMC Medical Education Pub Date : 2025-01-31 DOI:10.1186/s12909-025-06640-x

Yang Xiaowen, Ding Jingjing, Wang Biao, Zhang Shenzhong, Wu Yana

{"title":"Design strategies for artificial intelligence based future learning centers in medical universities.","authors":"Yang Xiaowen, Ding Jingjing, Wang Biao, Zhang Shenzhong, Wu Yana","doi":"10.1186/s12909-025-06640-x","DOIUrl":null,"url":null,"abstract":"Background: This study explores the acceptance of artificial intelligence(AI) tools in medical students and its influencing factors, thus providing theoretical basis and practical guidance for the construction of future learning centers in medical universities.Methods: This study comprehensively applied the unified theory of acceptance and use of technology(UTAUT), expectancy confirmation theory (ECT), and innovation diffusion theory (IDT) to analyze the data through structural equation modeling.Results: Effort expectancy (EE), facilitating condition (FC), social influence (SI), and satisfaction (SA) significantly influence medical students' continuance intention (CI) to use artificial intelligence tools. Relative advantage (RA) has a significant impact on medical students' satisfaction (SA) with artificial intelligence tools. Personal innovativeness (PI) plays a significant positive moderating role in the relationships between facilitating condition (FC) and continuance intention (CI), as well as between satisfaction (SA) and continuance intention (CI).Conclusions: The construction of AI-based future learning centers in medical universities should attach importance to providing personalized learning paths, ensuring technical support and training, creating a collaborative and innovative environment, and showcasing the comparative advantage of tools.","PeriodicalId":51234,"journal":{"name":"BMC Medical Education","volume":"25 1","pages":"161"},"PeriodicalIF":2.7000,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11786482/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Education","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12909-025-06640-x","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}

引用次数: 0

Abstract

Background: This study explores the acceptance of artificial intelligence(AI) tools in medical students and its influencing factors, thus providing theoretical basis and practical guidance for the construction of future learning centers in medical universities.

Methods: This study comprehensively applied the unified theory of acceptance and use of technology(UTAUT), expectancy confirmation theory (ECT), and innovation diffusion theory (IDT) to analyze the data through structural equation modeling.

Results: Effort expectancy (EE), facilitating condition (FC), social influence (SI), and satisfaction (SA) significantly influence medical students' continuance intention (CI) to use artificial intelligence tools. Relative advantage (RA) has a significant impact on medical students' satisfaction (SA) with artificial intelligence tools. Personal innovativeness (PI) plays a significant positive moderating role in the relationships between facilitating condition (FC) and continuance intention (CI), as well as between satisfaction (SA) and continuance intention (CI).

Conclusions: The construction of AI-based future learning centers in medical universities should attach importance to providing personalized learning paths, ensuring technical support and training, creating a collaborative and innovative environment, and showcasing the comparative advantage of tools.

查看原文本刊更多论文

医科大学基于人工智能的未来学习中心的设计策略。

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

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.