人工智能赋能医学生创造力研究

    Research on artificial intelligence enabling medical students to be creative

    • 摘要:
      目的: 探讨人工智能(AI)对医学生创造力影响的内在机制。
      方法: 基于刺激—有机体—反应理论模型,采用一般资料调查表、感知AI系统特征量表、感知AI提供支持量表和感知AI影响创造力量表对安徽省6所本科院校的医学生进行横断面调查。
      结果: 各量表的Cronbach's α系数均>0.8,平均提取方差值均>0.5,组合信度均>0.7,每个题的因子载荷均>0.6,方差膨胀系数均<5,每题的平均提取方差值均大于其与所有其他题的平方相关性。医学生感知AI系统特征量表、感知AI提供支持量表和感知AI影响创造力量表的得分均在3分以上。感知系统特征的准确性、智能型、可解释性和感知提供支持的信息支持、启发支持、评估支持及感知AI影响创造力7个研究变量之间均呈明显正相关关系(P < 0.01)。结构方程模型验证了7因子模型,拟合指数分别为:χ2 = 259.111,χ2/df = 1.713,RMSEA = 0.049,GFI = 0.959,AGFI = 0.971,CFI = 0.982,NFI = 0.970,IFI = 0.976;产生了准确性→信息支持→创造力、准确性→评估支持→创造力、智能性→信息支持→创造力、智能性→启发支持→创造力、智能性→评估支持→创造力和可解释性→评估支持→创造力6条中介路径,均通过了中介检验。
      结论: 本研究探明了AI技术使用对在校医学生创造力影响的内在机制,对于医学生使用AI辅助学习和科研具有实践意义。

       

      Abstract:
      Objective To explore the internal mechanism of the influence of artificial intelligence (AI) on medical students' creativity.
      Methods Based on the stimulus-organic-response theoretical model, a cross-sectional survey of medical students in 6 undergraduate colleges in Anhui province was conducted using general data questionnaire, perceived AI system characteristics scale, perceived AI providing support scale and perceived AI influencing creativity scale.
      Results Cronbach's α coefficient of all scales was more than 0.8, average variance extracted was more than 0.5, composite reliability was more than 0.7, factor loading of each question was more than 0.6, variance inflation factor was less than 5, and average variance extracted of each question was more than its square correlation with all other questions. The scores of medical students' perceived AI system characteristics scale, perceived AI providing support scale and perceived AI influencing creativity scale were all above 3 points. The accuracy, intelligence and interpretability of perceived system characteristics, information support, heuristic support and evaluation support of perceived providing support, and perceived AI influencing creativity were positively correlated with each other (P < 0.01). The structural equation model verified the 7-factor model with the fitting indexes as follows: χ2 = 259.111, χ2/df = 1.713, RMSEA = 0.049, GFI = 0.959, AGFI = 0.971, CFI = 0.982, NFI = 0.970, IFI = 0.976, and produced six mediating paths of accuracy → information support → creativity, accuracy → evaluation support → creativity, intelligence → information support → creativity, intelligence → heuristic support → creativity, intelligence → evaluation support → creativity and interpretability → evaluation support → creativity which passed the mediation test.
      Conclusions This study verifies the internal mechanism of the influence of AI technology use on the creativity of medical students in school, which has practical significance for medical students to use AI to assist learning and scientific research.

       

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