According to constructivist theory, students learn software security more effectively when examples are grounded in their own code. Generic examples often fail to connect with students' prior work, limiting engagement and understanding. Advances in LLMs are now making it possible to automatically generate personalized examples by embedding security vulnerabilities directly into student-authored code. This paper introduces a method that uses LLMs to inject instances of specific Common Weakness Enumerations (CWEs) into students' own assignment code, creating individualized instructional materials. We present an agentic AI framework, using autonomous LLM-based agents equipped with task-specific tools to orchestrate injection, evaluation, ranking, and learning outcome generation. We report the experience of deploying this system in two undergraduate computer science courses (N=71), where students reviewed code samples containing LLM-injected vulnerabilities and completed a post-project survey. We compared responses with a baseline using a widely adopted set of generic security instructional materials. Students qualitatively reported finding CWE injections into their own code more relevant, clearer, and more engaging than the textbook-style examples. However, our quantitative findings revealed limited statistically significant differences, suggesting that while students valued the personalization, further studies and refinement of the approach are needed to establish stronger empirical support.

翻译：根据建构主义理论，当学习示例基于学生自身代码时，软件安全教学效果更为显著。通用示例往往难以关联学生已有编程成果，限制了参与度和理解深度。大语言模型的进步使得自动生成个性化示例成为可能——通过将安全漏洞直接嵌入学生编写的代码中。本文提出一种方法，利用大语言模型将特定通用弱点枚举（CWE）实例注入学生作业代码，从而创建个性化教学材料。我们构建了基于自主LLM代理的AI框架，配备任务专用工具，统筹执行漏洞注入、评估、排序及学习成果生成。在本科计算机科学两门课程（N=71）中部署该系统后，学生审查了包含LLM注入漏洞的代码样本并完成项目后问卷调查。我们将反馈与采用通用安全教学材料的基线组进行对比。定性结果表明，学生认为注入自己代码的CWE实例相比教科书式示例更具相关性、清晰度和参与感。然而定量分析显示统计显著性差异有限，说明虽然学生认可个性化价值，但仍需进一步研究和方法优化以建立更强有力的实证支持。