Linking learning resources to a structured competency framework is key to enabling competency-based search and curriculum analytics in Learning Management Systems (LMS). However, manual tagging is labor-intensive, and fully automatic methods often lack transparency. In this paper, we present an end-to-end alignment pipeline that uses a large language model (LLM) as a constrained, evidence-producing tagger. LMS resources -both instructional content and assessments -are first segmented into meaningful pedagogical fragments. For each fragment, a small set of candidate competencies is retrieved from structured competency profiles enriched with graph-based context. The LLM then selects the most relevant competencies from this set and provides supporting evidence spans from the fragment text. These predictions are refined using the structure of the competency graph and aggregated at the resource level. We evaluate our approach on a dataset built from the Computer Science department's competency referential at the Université de Technologie de Compiègne (UTC), covering 22 competencies across multiple course materials. Our LLM+BM25+Graph (LBG) pipeline achieves strong results, with a micro-F1 of 0.57 and macro-F1 of 0.50 at the fragment level, 0.51 macro-F1 at the resource level, and an MRR of 0.82outperforming zero-shot and few-shot LLM variants, retrieval/similarity baselines, and supervised classifiers -while also producing more mechanically traceable evidence spans to support human auditing and educational analysis.

翻译：将学习资源与结构化能力框架关联是实现学习管理系统（LMS）中能力导向搜索和课程分析的关键。然而，人工标注劳动密集，全自动方法常缺乏透明度。本文提出一种端到端对齐流水线，利用大型语言模型（LLM）作为受约束的、能生成证据的标注器。LMS资源——包括教学内容和评估材料——首先被分割为有意义的教学片段。对每个片段，从富含图结构上下文的能力档案中检索少量候选能力。LLM随后从这些候选中选择最相关的能力，并从片段文本中提取支持性证据片段。这些预测结果通过能力图结构进行精炼，并在资源层面进行聚合。我们基于法国贡比涅技术大学（UTC）计算机科学系能力参考体系构建的数据集进行评估，该数据集覆盖22项跨多门课程材料的能力。我们提出的LLM+BM25+Graph（LBG）流水线取得了优异成绩：片段级微观F1值为0.57、宏观F1值为0.50，资源级宏观F1值为0.51，平均倒数排名（MRR）为0.82——不仅超越了零样本和少样本LLM变体、检索/相似度基线以及有监督分类器，还能生成更易机械追踪的证据片段，以支持人工审计和教育分析。