人工智能驱动下职业院校个性化教学能力构建研究

Research on the Development of Personalized Teaching Capacity in Vocational Education Institutions under the Driving Force of Artificial Intelligence

摘要: 随着教育部于2024年启动“人工智能赋能教育行动”，我国教育数字化战略进入集成化、智能化、国际化的新阶段。职业教育作为培养技术技能人才的重要阵地，在推进“以学生为中心”的教学改革过程中，面临资源碎片化、教师数智素养不足和评价机制单一等现实问题，个性化教学需求日益凸显。与对AI赋能职业教育的泛化讨论不同，本文将研究范围聚焦于职业院校个性化教学实施链条中的三个核心环节：学生画像与学习分析、职业技能训练与实践教学、智能辅导与学习反馈，并将职业规划与就业支持作为延伸应用加以讨论。文章以认知负荷理论和活动理论为分析支撑，探讨AI如何通过学习数据分析、自适应任务推送、智能反馈与虚拟仿真等方式提升教学适配性与学习效率，同时深入分析数据偏见、算法公平性、过度依赖AI、高阶思维弱化、教师角色弱化以及数据安全与数字鸿沟等风险挑战。研究认为，职业教育中的AI应用应坚持“教师主导、学生主体、技术赋能、治理护航”的基本原则，在提升个性化教学质量的同时守住教育公平、教育伦理与育人价值底线。

Abstract: With the launch of the “Artificial Intelligence Empowering Education Initiative” by the Ministry of Education in 2024, China’s educational digitalization strategy has entered a new stage characterized by integration, intelligence, and internationalization. As a key sector for cultivating technical and skilled talent, vocational education faces fragmented resources, insufficient digital and intelligent competencies among teachers, and overly singular evaluation mechanisms during student-centered reform, which makes personalized teaching increasingly urgent. Rather than broadly covering all possible AI scenarios, this study focuses on three core links in the implementation of personalized teaching in vocational institutions: student profiling and learning analytics, skill training and practical instruction, and intelligent tutoring and learning feedback, while treating career guidance and employment support as extended applications. Drawing on cognitive load theory and activity theory, the paper analyzes how AI can improve instructional adaptation and learning efficiency through learning data analysis, adaptive task recommendation, intelligent feedback, and virtual simulation. It also critically discusses major risks and challenges, including data bias and fairness, over-reliance on AI, the weakening of higher-order thinking, the risk of marginalizing teachers, and issues of data security and digital divide. The study argues that AI application in vocational education should follow the principle of teacher-led, student-centered, technology-empowered, and governance-supported development.