{
  "id": 960,
  "url": "https://arxiv.org/abs/2606.16626v1",
  "title": "Using AI in engineering education: a balancing act, driven by clear purpose",
  "summary": "Based on a questionnaire of 100 higher-education students, predominantly from engineering-related fields, and a critical review of recent literature, this chapter examines how students use and perceive Large Language Models (LLMs) in engineering education. Students primarily value LLMs for writing support, conceptual clarification, coding assistance, and brainstorming, while simultaneously expressing concerns about inaccuracies, bias, overreliance, academic integrity, and the burden of verificat",
  "authors": "Olya Kudina",
  "category": "research",
  "topics": "bias-fairness,children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-15T12:21:42.000Z",
  "fetched_at": "2026-07-14T14:14:54.534Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/960",
  "original_url": "https://arxiv.org/abs/2606.16626v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}