Frontiers in public health · 2026
Artificial intelligence readiness and its association with artificial intelligence literacy among Chinese medical students: a latent profile analysis.
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Tang Zongsheng, Song Shuhong, Chen Xinyue, Qin Jiaxu, Li Yan, Yu Jingjing, Yu Chen, Liu Huan, Tao Xiangxiang
Аннотация
OBJECTIVES: To identify heterogeneous subgroups of medical students' artificial intelligence readiness through latent profile analysis and to examine their associations with artificial intelligence literacy.
METHODS: A cross-sectional survey was conducted from February to May 2026 among 707 medical students from four universities in Anhui Province, China, using the Medical Artificial Intelligence Readiness Scale and the Artificial Intelligence Literacy Scale. Latent profile analysis was performed using the 22 artificial intelligence readiness items as manifest indicators. Model selection was based on information criteria, entropy, likelihood-ratio tests, profile size, posterior classification probabilities, parsimony, and interpretability. Chi-square tests, one-way analysis of variance, and multinomial logistic regression were used for exploratory profile comparisons.
RESULTS: Latent profile analysis identified three distinct artificial intelligence readiness profiles: low (n = 342, 48.4%), moderate (n = 293, 41.4%), and high (n = 72, 10.2%). Multinomial logistic regression revealed that frequent exposure to artificial intelligence-medicine interdisciplinary courses, interest in artificial intelligence, and higher satisfaction with current major were significant predictors of belonging to higher readiness profiles. A significant graded association emerged, with high-readiness students demonstrating substantially superior artificial intelligence literacy across all dimensions compared to their moderate- and low-readiness counterparts.
CONCLUSION: Medical students exhibit substantial heterogeneity in artificial intelligence readiness, with nearly half demonstrating low preparedness. The pronounced deficits in practical ability and the strong linkage between readiness profiles and comprehensive artificial intelligence literacy underscore the urgent need for tiered, behavior-oriented curricular interventions that foster proactive engagement and hands-on artificial intelligence skills across the continuum of medical education.
METHODS: A cross-sectional survey was conducted from February to May 2026 among 707 medical students from four universities in Anhui Province, China, using the Medical Artificial Intelligence Readiness Scale and the Artificial Intelligence Literacy Scale. Latent profile analysis was performed using the 22 artificial intelligence readiness items as manifest indicators. Model selection was based on information criteria, entropy, likelihood-ratio tests, profile size, posterior classification probabilities, parsimony, and interpretability. Chi-square tests, one-way analysis of variance, and multinomial logistic regression were used for exploratory profile comparisons.
RESULTS: Latent profile analysis identified three distinct artificial intelligence readiness profiles: low (n = 342, 48.4%), moderate (n = 293, 41.4%), and high (n = 72, 10.2%). Multinomial logistic regression revealed that frequent exposure to artificial intelligence-medicine interdisciplinary courses, interest in artificial intelligence, and higher satisfaction with current major were significant predictors of belonging to higher readiness profiles. A significant graded association emerged, with high-readiness students demonstrating substantially superior artificial intelligence literacy across all dimensions compared to their moderate- and low-readiness counterparts.
CONCLUSION: Medical students exhibit substantial heterogeneity in artificial intelligence readiness, with nearly half demonstrating low preparedness. The pronounced deficits in practical ability and the strong linkage between readiness profiles and comprehensive artificial intelligence literacy underscore the urgent need for tiered, behavior-oriented curricular interventions that foster proactive engagement and hands-on artificial intelligence skills across the continuum of medical education.