{
  "id": 4697,
  "url": "https://arxiv.org/abs/2605.08486v1",
  "title": "Teachers' Perceived Benefits and Risks of AI Across Fifty-Five Countries: An Audit of LLM Alignment and Steerability",
  "summary": "Teachers' trust in artificial intelligence (AI) in education depends on how they balance its perceived benefits and risks. Yet global discussions about scaling AI in education rely on fragmented evidence, as most studies of teachers' perceptions focus on single countries or small samples. This lack of representative cross-national evidence limits both theory building and policy development. At the same time, large language models (LLMs) are increasingly used in research, policy, and teachers' pr",
  "authors": "Yan Tao, Olga Viberg, Deepak Varuvel Dennison, Zhikun Wu, René F. Kizilcec",
  "category": "research",
  "topics": "regulation,safety-alignment,children-education,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-08T21:03:25.000Z",
  "fetched_at": "2026-07-14T16:31:12.744Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4697",
  "original_url": "https://arxiv.org/abs/2605.08486v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}