{
  "id": 7070,
  "url": "https://arxiv.org/abs/2604.09622v1",
  "title": "Explainability and Certification of AI-Generated Educational Assessments",
  "summary": "The rapid adoption of generative artificial intelligence (AI) in educational assessment has created new opportunities for scalable item creation, personalized feedback, and efficient formative evaluation. However, despite advances in taxonomy alignment and automated question generation, the absence of transparent, explainable, and certifiable mechanisms limits institutional and accreditation-level acceptance. This chapter proposes a comprehensive framework for explainability and certification of",
  "authors": "Antoun Yaacoub, Zainab Assaghir, Anuradha Kar",
  "category": "research",
  "topics": "safety-alignment,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-18T11:33:58.000Z",
  "fetched_at": "2026-07-14T16:32:59.162Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7070",
  "original_url": "https://arxiv.org/abs/2604.09622v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}