Refining AI perspectives: assessing the impact of ai curricular on medical students’ attitudes towards artificial intelligence
Abstract This study explores the impact of Artificial Intelligence (AI) curricula on medical students’ perceptions of AI, a critical topic given AI’s transformative potential in healthcare and its rapid integration into medical practice and education. Using data from a global cross-sectional survey...
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BMC
2025-07-01
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| Series: | BMC Medical Education |
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| Online Access: | https://doi.org/10.1186/s12909-025-07669-8 |
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| author | Li Zheng Yu Xiao |
| author_facet | Li Zheng Yu Xiao |
| author_sort | Li Zheng |
| collection | DOAJ |
| description | Abstract This study explores the impact of Artificial Intelligence (AI) curricula on medical students’ perceptions of AI, a critical topic given AI’s transformative potential in healthcare and its rapid integration into medical practice and education. Using data from a global cross-sectional survey involving 4,596 students across 48 countries, we employed Coarsened Exact Matching (CEM) to address selection bias and Structural Equation Modeling (SEM) to examine mediating effects. Regression models were also applied to estimate the relationships between AI curricular and students’ knowledge about and attitudes towards AI. Results reveal that participation in AI curricula significantly enhances students’ knowledge about AI (β = .140, p < .001), equipping them with essential skills for AI-driven healthcare systems. However, it concurrently diminishes their enthusiasm for integrating AI into medical education (β = -.108, p < .001), reflecting potential concerns about ethical and professional implications. No significant effects were observed on students’ attitudes towards Artificial Intelligence application in medicine, the physician’s role, or AI-related ethical and legal conflicts. Heterogeneity analysis shows stronger positive effects on knowledge for veterinary students and those from developing countries, where AI education addresses critical resource gaps. Conversely, the negative effect on enthusiasm for AI teaching is more pronounced among students from developed countries, where advanced AI applications are more prevalent. SEM results reveal that preparedness for work with AI partially mediates the relationship between AI curricula and students' knowledge (β = .062, p < .001) and attitudes (β = .023, p < .001), adding theoretical depth to the findings. These results underscore the importance of balanced AI education to enhance knowledge while addressing concerns about its integration in education. This research has significant practical and theoretical implications, emphasizing the need for tailored AI curricula that align with students’ professional goals and regional educational contexts. The study offers pathways for optimizing AI literacy globally, bridging resource disparities, and preparing future healthcare professionals for AI-driven advancements. |
| format | Article |
| id | doaj-art-61d0756e28534bcd8ceabff45cbb0983 |
| institution | Kabale University |
| issn | 1472-6920 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Education |
| spelling | doaj-art-61d0756e28534bcd8ceabff45cbb09832025-08-20T03:45:56ZengBMCBMC Medical Education1472-69202025-07-0125111910.1186/s12909-025-07669-8Refining AI perspectives: assessing the impact of ai curricular on medical students’ attitudes towards artificial intelligenceLi Zheng0Yu Xiao1School of Education, Shanghai Normal UniversitySchool of Education, Tsinghua UniversityAbstract This study explores the impact of Artificial Intelligence (AI) curricula on medical students’ perceptions of AI, a critical topic given AI’s transformative potential in healthcare and its rapid integration into medical practice and education. Using data from a global cross-sectional survey involving 4,596 students across 48 countries, we employed Coarsened Exact Matching (CEM) to address selection bias and Structural Equation Modeling (SEM) to examine mediating effects. Regression models were also applied to estimate the relationships between AI curricular and students’ knowledge about and attitudes towards AI. Results reveal that participation in AI curricula significantly enhances students’ knowledge about AI (β = .140, p < .001), equipping them with essential skills for AI-driven healthcare systems. However, it concurrently diminishes their enthusiasm for integrating AI into medical education (β = -.108, p < .001), reflecting potential concerns about ethical and professional implications. No significant effects were observed on students’ attitudes towards Artificial Intelligence application in medicine, the physician’s role, or AI-related ethical and legal conflicts. Heterogeneity analysis shows stronger positive effects on knowledge for veterinary students and those from developing countries, where AI education addresses critical resource gaps. Conversely, the negative effect on enthusiasm for AI teaching is more pronounced among students from developed countries, where advanced AI applications are more prevalent. SEM results reveal that preparedness for work with AI partially mediates the relationship between AI curricula and students' knowledge (β = .062, p < .001) and attitudes (β = .023, p < .001), adding theoretical depth to the findings. These results underscore the importance of balanced AI education to enhance knowledge while addressing concerns about its integration in education. This research has significant practical and theoretical implications, emphasizing the need for tailored AI curricula that align with students’ professional goals and regional educational contexts. The study offers pathways for optimizing AI literacy globally, bridging resource disparities, and preparing future healthcare professionals for AI-driven advancements.https://doi.org/10.1186/s12909-025-07669-8Artificial intelligenceMedical studentsAI curricularCoarsened exact matchingStructural equation modeling |
| spellingShingle | Li Zheng Yu Xiao Refining AI perspectives: assessing the impact of ai curricular on medical students’ attitudes towards artificial intelligence BMC Medical Education Artificial intelligence Medical students AI curricular Coarsened exact matching Structural equation modeling |
| title | Refining AI perspectives: assessing the impact of ai curricular on medical students’ attitudes towards artificial intelligence |
| title_full | Refining AI perspectives: assessing the impact of ai curricular on medical students’ attitudes towards artificial intelligence |
| title_fullStr | Refining AI perspectives: assessing the impact of ai curricular on medical students’ attitudes towards artificial intelligence |
| title_full_unstemmed | Refining AI perspectives: assessing the impact of ai curricular on medical students’ attitudes towards artificial intelligence |
| title_short | Refining AI perspectives: assessing the impact of ai curricular on medical students’ attitudes towards artificial intelligence |
| title_sort | refining ai perspectives assessing the impact of ai curricular on medical students attitudes towards artificial intelligence |
| topic | Artificial intelligence Medical students AI curricular Coarsened exact matching Structural equation modeling |
| url | https://doi.org/10.1186/s12909-025-07669-8 |
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