Current status and quantitative evaluation of AI application diffusion and integration among college students
大学生群体AI应用扩散融合现状及定量评价

摘要

扩散理论为新技术的发展脉络提供了一个很好的视角。基于大学生群体人工智能技术掌握、学习应用、创新实践构建应用扩散融合评价指标体系，采用熵权-Topsis法评估目前大学生群体人工智能使用程度，并运用t-test与ANOVA比较不同人口统计学变量间程度的差异性。研究表明：（1）应用扩散融合程度整体处于中度略偏上水平；（2）相较于技术掌握与学习应用，创新实践对应用扩散融合程度的影响更为显著；（3）应用扩散融合程度在性别、年级、专业上无显著差异性。基于此，从强化人工智能创新实践，提升深度加工能力等方面提出推动人工智能技术在大学生群体中的应用扩散融合建议。

Abstract

Diffusion theory offers a valuable perspective on the development trajectory of new technologies. An application diffusion integration evaluation index system was constructed based on college students' mastery, learning application, and innovative practice of artificial intelligence technology. The Entropy Weight-Topsis method was employed to assess the current level of AI usage among college students, while t-tests and ANOVA were used to compare differences in usage levels across various demographic variables. The findings reveal: (1) The overall level of application diffusion integration is moderately high; (2) Compared to technical mastery and learning/application, innovative practice exerts a more significant influence on the degree of application diffusion integration; (3) No significant differences in application diffusion integration are observed across gender, grade level, or major. Based on these results, recommendations are proposed to promote the application diffusion integration of AI technology among university students, focusing on strengthening AI innovation practice and enhancing deep processing capabilities.