人工智能融合交叉技术推动嵌入式课程教学改革研究

Research on the Integration of Artificial Intelligence and Cross Disciplinary Technologies to Promote the Reform of Embedded Course Teaching

摘要: 本文针对传统嵌入式课程教学中存在的理论与实践脱节、技术更新滞后等问题，提出将人工智能与机器视觉、大数据等交叉技术深度融合，构建面向智能时代的嵌入式系统教学改革框架。研究首先重构课程体系，增设AI算法轻量化部署、边缘智能开发等模块，并引入虚拟仿真平台与真实硬件结合的“双轨实训”模式；其次，设计跨学科项目案例库，如基于深度学习的嵌入式视觉系统开发，培养学生解决复杂工程问题的能力；同时，搭建智能评测系统，通过代码语义分析、学习行为追踪等技术实现个性化学习路径推荐。实践表明，该模式使学生的系统设计能力提升32%，创新项目参与度提高45%，为培养符合产业需求的复合型嵌入式人才提供有效路径。研究结果对工程教育数字化转型具有示范意义。

Abstract: This article proposes to deeply integrate artificial intelligence with cross disciplinary technologies such as machine vision and big data, in order to construct a reform framework for embedded system teaching in the intelligent era, addressing the problems of theoretical and practical disconnection and lagging technological updates in traditional embedded course teaching. The research first reconstructs the curriculum system, adds modules such as AI algorithm lightweight deployment and edge intelligence development, and introduces a “dual track training” mode that combines virtual simulation platforms with real hardware; Secondly, design interdisciplinary project case libraries, such as the development of embedded visual systems based on deep learning, to cultivate students’ ability to solve complex engineering problems; At the same time, an intelligent evaluation system will be built to achieve personalized learning path recommendations through technologies such as code semantic analysis and learning behavior tracking. Practice has shown that this model improves students’ system design ability by 32% and innovation project participation by 45%, providing an effective path for cultivating composite embedded talents that meet industry needs. The research results have demonstrative significance for the digital transformation of engineering education.