创新创业理论研究与实践 ›› 2026, Vol. 9 ›› Issue (9): 175-178.

• 创业实践 • 上一篇    下一篇

AI赋能视域下高校“双创”教育实践教学模块的智能重构路径研究——以大学生创新创业基础课程为例

余涛, 柳薇, 于柏杨   

  1. 绵阳城市学院,四川绵阳 621000
  • 出版日期:2026-05-10 发布日期:2026-06-30
  • 作者简介:余涛(1982—),女,羌族,四川绵阳人,硕士研究生,副教授,研究方向:大学生职业规划、就业指导、农村经济发展等。
  • 基金资助:
    2025年绵阳城市学院校级教育教学改革研究项目“AI赋能视域下高校双创教育实践教学模块的智能重构与创新路径研究——以‘大学生创新创业基础’课程为例”(CC-25JJ06)

Research on the Intelligent Reconstruction Path of the Practical Teaching Module of “Double Innovation” Education in Universities from the Perspective of AI Empowerment—Taking the Basic Course of Innovation and Entrepreneurship for College Students as an Example

YU Tao, LIU Wei, YU Baiyang   

  1. Mianyang City College, Mianyang Sichuan, 621000, China
  • Online:2026-05-10 Published:2026-06-30

摘要: 该文以绵阳城市学院大学生创新创业基础课程为例,探讨了AI技术赋能高校“双创”教育实践教学模块的智能重构与创新路径,通过引入智能教学平台、个性化学习系统和智能评价体系,实现了教学资源的优化配置、学习过程的精准管理以及教学效果的科学评估。研究结果表明:AI技术的应用显著提升了学生的学习兴趣和创新创业能力,同时减轻了教师的教学负担,为高校“双创”教育的改革与创新提供了可推广的模式和经验。

关键词: AI赋能, “双创”教育, 实践教学, 智能重构, 创新路径, 个性化学习, 知识图谱

Abstract: This article takes the innovation and entrepreneurship foundation course for college students at Mianyang City College as an example to explore the intelligent reconstruction and innovation path of AI technology empowering the practical teaching module of innovation and entrepreneurship education in universities. By introducing intelligent teaching platforms, personalized learning systems, and intelligent evaluation systems, has achieved optimized allocation of teaching resources, precise management of learning processes, and scientific evaluation of teaching effectiveness. The research results indicate that the application of AI technology significantly enhances students'learning interest and innovation and entrepreneurship abilities, while reducing the teaching burden on teachers, providing a scalable model and experience for the reform and innovation of entrepreneurship education in universities.

Key words: AI empowerment, “Double innovation”education, Practical teaching, Intelligent reconstruction, Innovation path, Personalized learning, Knowledge graph

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