{
  "id": 6763,
  "url": "https://arxiv.org/abs/2603.24389v1",
  "title": "When AI Meets Early Childhood Education: Large Language Models as Assessment Teammates in Chinese Preschools",
  "summary": "High-quality teacher-child interaction (TCI) is fundamental to early childhood development, yet traditional expert-based assessment faces a critical scalability challenge. In large systems like China's-serving 36 million children across 250,000+ kindergartens-the cost and time requirements of manual observation make continuous quality monitoring infeasible, relegating assessment to infrequent episodic audits that limit timely intervention and improvement tracking. In this paper, we investigate w",
  "authors": "Xingming Li, Runke Huang, Yanan Bao, Yuye Jin, Yuru Jiao, Qingyong Hu",
  "category": "research",
  "topics": "privacy-surveillance,children-education,transparency,finance-investment",
  "orgs": null,
  "regions": "china",
  "published_at": "2026-03-25T15:05:34.000Z",
  "fetched_at": "2026-07-14T16:32:45.891Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6763",
  "original_url": "https://arxiv.org/abs/2603.24389v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}