{
  "id": 13594,
  "url": "https://arxiv.org/abs/2604.27618",
  "title": "Math Education Digital Shadows for Investigating Learning with GenAI: Mathematics Performance, Anxiety, and Confidence in LLMs",
  "summary": "arXiv:2604.27618v2 Announce Type: replace-cross Abstract: Understanding the impact of large language models (LLMs) on mathematics education requires data on LLMs' mathematical performance and biases. To this end, we introduce Math Education Digital Shadows (MEDS), a dataset mapping how LLMs reason about mathematics across human- and AI-like personifications. MEDS comprises 28,000 runs from 14 LLMs (i.e., Mistral, Qwen, DeepSeek, IBM Granite, Microsoft Phi, and xAI Grok) generated under human-sha",
  "authors": "Naomi Esposito, Anthony Tricarico, Luisa Porzio, Ali Aghazadeh Ardebili, Massimo Stella",
  "category": "research",
  "topics": "children-education,finance-investment",
  "orgs": "xai,microsoft,mistral,deepseek",
  "regions": null,
  "published_at": "2026-07-27T04:00:00.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "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/13594",
  "original_url": "https://arxiv.org/abs/2604.27618",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}