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. 2024 Jun 7;24(1):644.
doi: 10.1186/s12909-024-05627-4.

AI in medical education: the moderating role of the chilling effect and STARA awareness

Meijie Wu #  1 , Xuefeng Huang #  1 , Baona Jiang  2 , Zhihong Li  3 , Yuanyuan Zhang  4 , Bo Gao  5
Affiliations

AI in medical education: the moderating role of the chilling effect and STARA awareness

Meijie Wu et al. BMC Med Educ. .

Abstract

Background: The rapid growth of artificial intelligence (AI) technologies has been driven by the latest advances in computing power. Although, there exists a dearth of research on the application of AI in medical education.

Methods: this study is based on the TAM-ISSM-UTAUT model and introduces STARA awareness and chilling effect as moderating variables. A total of 657 valid questionnaires were collected from students of a medical university in Dalian, China, and data were statistically described using SPSS version 26, Amos 3.0 software was used to validate the research model, as well as moderated effects analysis using Process (3.3.1) software, and Origin (2021) software.

Results: The findings reveal that both information quality and perceived usefulness are pivotal factors that positively influence the willingness to use AI products. It also uncovers the moderating influence of the chilling effect and STARA awareness.

Conclusions: This suggests that enhancing information quality can be a key strategy to encourage the widespread use of AI products. Furthermore, this investigation offers valuable insights into the intersection of medical education and AI use from the standpoint of medical students. This research may prove to be pertinent in shaping the promotion of Medical Education Intelligence in the future.

Keywords: Artificial intelligence (AI); Chilling effect; Intention to continue to use; Medical education; STARA awareness.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Extended TAM-ISSM-UTAUT integration model
Fig. 2
Fig. 2
Experimental design and sample selection
Fig. 3
Fig. 3
Demographic characteristics sankey diagram
Fig. 4
Fig. 4
Results of TAM-ISSM-UTAUT model. (CMIN/df = 4.175, IFI = 0.795, CFI = 0.794, RMSEA = 0.07)
Fig. 5
Fig. 5
(a) Mediation effect test results of CE. Fig. 5b Mediation effect test results of SA
Fig. 6
Fig. 6
AI in medical education network
See this image and copyright information in PMC

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