{
  "id": 14512,
  "url": "https://arxiv.org/abs/2607.26067",
  "title": "The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty",
  "summary": "arXiv:2607.26067v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for estimating item difficulty in educational assessment. However, it remains unclear whether such estimates reflect how learners actually experience difficulty. This study investigates the alignment between LLM-generated difficulty ratings and empirical student performance on basic mathematics tasks. Four widely used LLM-based systems generated difficulty ratings on a 1-100 scale for 32 arithmetic",
  "authors": "Amanda La Hadi, Muhammad Johan Alibasa, Guanliang Chen, A. Taufiq Asyhari",
  "category": "research",
  "topics": "safety-alignment,children-education,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-30T04:00:00.000Z",
  "fetched_at": "2026-07-30T05:10:24.387Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14512",
  "original_url": "https://arxiv.org/abs/2607.26067",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}