{
  "id": 4614,
  "url": "https://arxiv.org/abs/2605.09624v1",
  "title": "Preparing Students for AI-Powered Materials Discovery: A Workflow-Aligned Framework for AI Literacy, Equity, and Scientific Judgment",
  "summary": "Artificial intelligence (AI) is reshaping education, scientific training, and materials discovery. In materials science, AI models increasingly support property prediction, experiment prioritization, and hypothesis generation; however, the limiting factor is no longer only algorithmic capability but also whether students and educators can use AI with domain-specific scientific judgment. This workshop-informed white paper and curriculum-oriented position article argues that AI education for AI-po",
  "authors": "Dongming Mei, Katherine Moore, Ben Sayler",
  "category": "research",
  "topics": "bias-fairness,children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-10T16:11:01.000Z",
  "fetched_at": "2026-07-14T16:31:08.355Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4614",
  "original_url": "https://arxiv.org/abs/2605.09624v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}