{
  "id": 3589,
  "url": "https://arxiv.org/abs/2605.28483v1",
  "title": "From Learning Resources to Competencies: LLM-Based Tagging with Evidence and Graph Constraints",
  "summary": "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 meanin",
  "authors": "Ngoc Luyen Le, Marie-Hélène Abel, Bertrand Laforge",
  "category": "research",
  "topics": "safety-alignment,jobs-economy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-27T13:41:10.000Z",
  "fetched_at": "2026-07-14T16:30:23.245Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3589",
  "original_url": "https://arxiv.org/abs/2605.28483v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}