Adaptive programming practice often relies on fixed libraries of worked examples and practice problems, which require substantial authoring effort and may not correspond well to the logical errors and partial solutions students produce while writing code. As a result, students may receive learning content that does not directly address the concepts they are working to understand, while instructors must either invest additional effort in expanding content libraries or accept a coarse level of personalization. We present an approach for knowledge-component (KC) guided educational content generation using pattern-based KCs extracted from student code. Given a problem statement and student submissions, our pipeline extracts recurring structural KC patterns from students' code through AST-based analysis and uses them to condition a generative model. In this study, we apply this approach to worked example generation, and compare baseline and KC-conditioned outputs through expert evaluation. Results suggest that KC-conditioned generation improves topical focus and relevance to learners' underlying logical errors, providing evidence that KC-based steering of generative models can support personalized learning at scale.

翻译：自适应编程练习通常依赖于固定库的示例和练习题，这需要大量的编写工作，且可能无法很好地对应学生在编写代码时产生的逻辑错误和部分解决方案。因此，学生可能接收到并非直接针对他们正在理解的概念的学习内容，而教师要么需要投入额外精力扩充内容库，要么只能接受粗粒度的个性化程度。我们提出了一种基于知识组件的教育内容生成方法，该方法使用从学生代码中提取的基于模式的知识组件。给定问题描述和学生的提交，我们的流水线通过基于抽象语法树的分析从学生代码中提取重复出现的结构知识组件模式，并利用这些模式来条件化一个生成模型。在本研究中，我们将该方法应用于示例生成，并通过专家评估比较了基线输出和知识组件条件化输出。结果表明，知识组件条件化生成改善了主题聚焦性和与学习者潜在逻辑错误的相关性，为基于知识组件的生成模型引导可支持大规模个性化学习提供了证据。