Design strategies for artificial intelligence based future learning centers in medical universities
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 comprehensi...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Education |
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| Online Access: | https://doi.org/10.1186/s12909-025-06640-x |
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| author | Yang Xiaowen Ding Jingjing Wang Biao Zhang Shenzhong Wu Yana |
| author_facet | Yang Xiaowen Ding Jingjing Wang Biao Zhang Shenzhong Wu Yana |
| author_sort | Yang Xiaowen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e403ff0dee7e4b179e8afe48e2e611a4 |
| institution | Kabale University |
| issn | 1472-6920 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Education |
| spelling | doaj-art-e403ff0dee7e4b179e8afe48e2e611a42025-02-02T12:29:55ZengBMCBMC Medical Education1472-69202025-01-0125111510.1186/s12909-025-06640-xDesign strategies for artificial intelligence based future learning centers in medical universitiesYang Xiaowen0Ding Jingjing1Wang Biao2Zhang Shenzhong3Wu Yana4Nanjing Medical University LibraryNanjing Medical University Institute of Medical Education ResearchNanjing Medical University LibraryNanjing Medical University LibraryNanjing Medical University The First Clinical Medical CollegeAbstract 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.https://doi.org/10.1186/s12909-025-06640-xArtificial intelligenceMedical universitiesFuture learning centerUnified theory of acceptance and use of technology(UTAUT)Expectancy confirmation theory (ECT)Innovation diffusion theory (IDT) |
| spellingShingle | Yang Xiaowen Ding Jingjing Wang Biao Zhang Shenzhong Wu Yana Design strategies for artificial intelligence based future learning centers in medical universities BMC Medical Education Artificial intelligence Medical universities Future learning center Unified theory of acceptance and use of technology(UTAUT) Expectancy confirmation theory (ECT) Innovation diffusion theory (IDT) |
| title | Design strategies for artificial intelligence based future learning centers in medical universities |
| title_full | Design strategies for artificial intelligence based future learning centers in medical universities |
| title_fullStr | Design strategies for artificial intelligence based future learning centers in medical universities |
| title_full_unstemmed | Design strategies for artificial intelligence based future learning centers in medical universities |
| title_short | Design strategies for artificial intelligence based future learning centers in medical universities |
| title_sort | design strategies for artificial intelligence based future learning centers in medical universities |
| topic | Artificial intelligence Medical universities Future learning center Unified theory of acceptance and use of technology(UTAUT) Expectancy confirmation theory (ECT) Innovation diffusion theory (IDT) |
| url | https://doi.org/10.1186/s12909-025-06640-x |
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