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Medical students' attitudes, usage patterns, and associated factors with DeepSeek adoption in education: a
Wei Liu1, Lei-Lei Cao1, Xiao-Yu Chen2
1School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China.
Background:
With the growing integration of generative Artificial Intelligence (AI) into healthcare, the DeepSeek large language model has emerged as a versatile tool for medical students, offering adaptive learning solutions tailored to various medical scenarios. However, the adoption of AI in medicine also raises ethical concerns that require warrant consideration. This study aimed to investigate medical students' usage patterns, attitudes, and influencing factors related to DeepSeek.
Methods:
In July 2025, a cross-sectional survey was conducted using random sampling among 1000 medical students from institutions affiliated with Anhui Medical University. Based on previous AI attitude scales, a validated self-administered questionnaire was used to collect data on students' attitudes, DeepSeek usage patterns.
Results:
Among the 937 valid responses (response rate: 93.7%), 874 participants were aware of DeepSeek prior to the survey and 765 had used it. The Cronbach's alpha value for the questionnaire was 0.83. 42.0% of students doubted the accuracy of information provided by DeepSeek, 48.6% were apprehensive about potential plagiarism accusations, and 43.0% worried about over-reliance on AI. Additionally, 46.6% found DeepSeek interesting and appealing, with 37.7% expressing enthusiasm for learning new AI technologies. Several factors were significantly associated with usage experience, including being female [β = 6.1(1.5-10.7), (p = 0.031)], holding a Master's degree [β = 10.5(2.1-18.1), (p = 0.044)], and engaging in clinical [β = 7.4(1.2-13.6), (p = 0.021)] or basic research [β = 9.2(2.9-15.4), (p = 0.012)]. Regarding attitudes, significant predictors included a Master's degree [β = -2.9(-6.2 to-0.5), (p = 0.011)], having a stomatology background [β = 8.0(2.3-13.7), (p = 0.035)], engaging in clinical [β = -2.6(-5.0 to -0.1), (p = 0.036)] or basic [β = -2.7(-5.2 to -0.3), (p = 0.042)] research.
Conclusions:
Among the medical students surveyed in this study, 94.8% reported that they would be willing to use and recommend DeepSeek, but they maintain a cautious attitude toward its privacy, reliability, and dependency. Despite these concerns, 90.2% of participants said they would be willing to use DeepSeek to help them complete their tasks.