{
  "id": 10927,
  "url": "https://arxiv.org/abs/2607.13370v1",
  "title": "Learning Engagement Assistant (LEA): Cross-Course Scalability and Classroom Evaluation of an Agentic AI Tutoring System",
  "summary": "This paper is an extension of a paper presented at the ICAART 2026 conference, which introduced LEA (Learning Engagement Assistant), an adaptive AI tutoring agent combining course-specific Retrieval-Augmented Generation (RAG) with structured Knowledge Component (KC) models across integrated Chat, Tutor, and Quiz modes. That prior work validated LEA on a single STEM course (CMP511) exclusively through simulation, using synthetic learner agents. This paper extends that work by reporting the first ",
  "authors": "Teri Rumble, Javad Zarrin, P. George Lovell, Ruth Falconer",
  "category": "research",
  "topics": "children-education,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-15T01:25:22.000Z",
  "fetched_at": "2026-07-16T05:10:56.605Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/10927",
  "original_url": "https://arxiv.org/abs/2607.13370v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}