工程教育认证背景下AI赋能信息安全专业人才培养模式的创新与实践探索研究

Innovation and Practice Exploration in AI-Enabled Talent Cultivation Models for Information Security under the Framework of Engineering Education Accreditation

摘要: 信息安全专业已完成课程资料和在线课程方面建设，但与信息安全专业工程认证的培养目标仍有一定差距，为提升人才培养质量，本文在工程认证背景下针对AI赋能信息安全专业人才培养模式的创新与实践探索研究。使用已有在线学习数据训练生成学生学业分层模型，对每个学生通过实时在线数据实现分类预测，通过学习路径自动生成算法对每个学生实现个性化指导。本算法在专业课程中，积极采用AI技术，实现学生为中心、产出为导向、持续改进的OBE理念，不断提升学生培养效果。实验验证，本算法能实现学生学业分层模型训练，对个体数据完成分类预测，并根据学生情况完成个性化指导。

Abstract: Although the information security has completed the construction of course materials and online courses, a gap still remains between its current status and the cultivation objectives stipulated by engineering education accreditation. To enhance the quality of talent cultivation, this study proposes an innovative AI-empowered talent cultivation model for the information security program under the framework of engineering accreditation. Specifically, a student academic stratification model is trained using existing online learning data to perform real-time classification of each student based on their online learning records; a learning-path automatic generation algorithm is then employed to provide personalized guidance for individual students. By integrating AI technologies into specialized courses, the proposed model implements the student-centered, outcome-oriented, and continuous-improvement principles of Outcome-Based Education (OBE), thereby steadily improving the effectiveness of talent cultivation. Experimental results demonstrate that the proposed algorithm successfully accomplishes stratification model training, achieves accurate classification prediction on individual student data, and delivers personalized guidance tailored to each student’s specific situation.