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<p>This study aims to address the needs of higher education teaching innovation, providing students with personalized learning resources and paths to effectively enhance teaching efficiency. The study proposes an Artificial Intelligence (AI)-integrated data-driven solution, completing the transformation from traditional to intelligent teaching by constructing a multimodal educational data ecosystem, a dynamic learning early-warning model, and a knowledge graph dual-tower recommendation system. The results show that: The course pass rate of the experimental group exceeds 93%, and the knowledge retention rate is 74.8%. The multimodal behavior analysis has a positive effect on improving early-warning timeliness (response time reduced to 2.3 days). The dual-tower recommendation structure optimizes cognitive matching (the score gain of experimental group students is 21.5 points). The above experiments verify the effectiveness of data-driven and AI fusion in promoting educational equity and achieving large-scale teaching according to aptitude, providing specific guidance and reference for the construction of smart education.</p>

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Teaching innovation mechanism of higher education driven by AI fusion data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月17日

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  • Zhen Chen,
  • Shirong Qin,
  • Hui Han,
  • Wei Peng,
  • Hua Liang

摘要

This study aims to address the needs of higher education teaching innovation, providing students with personalized learning resources and paths to effectively enhance teaching efficiency. The study proposes an Artificial Intelligence (AI)-integrated data-driven solution, completing the transformation from traditional to intelligent teaching by constructing a multimodal educational data ecosystem, a dynamic learning early-warning model, and a knowledge graph dual-tower recommendation system. The results show that: The course pass rate of the experimental group exceeds 93%, and the knowledge retention rate is 74.8%. The multimodal behavior analysis has a positive effect on improving early-warning timeliness (response time reduced to 2.3 days). The dual-tower recommendation structure optimizes cognitive matching (the score gain of experimental group students is 21.5 points). The above experiments verify the effectiveness of data-driven and AI fusion in promoting educational equity and achieving large-scale teaching according to aptitude, providing specific guidance and reference for the construction of smart education.