{
  "id": 6124,
  "url": "https://arxiv.org/abs/2604.08263v1",
  "title": "Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modelling",
  "summary": "The growing use of artificial intelligence (AI) in education, particularly large language models (LLMs), has increased interest in intelligent tutoring systems. However, LLMs often show limited adaptivity and struggle to model learners' evolving knowledge over time, highlighting the need for dedicated learner modelling approaches. Although deep knowledge tracing methods achieve strong predictive performance, their opacity and susceptibility to bias can limit alignment with pedagogical principles",
  "authors": "Danial Hooshyar, Gustav Šír, Yeongwook Yang, Tommi Kärkkäinen, Raija Hämäläinen, Ekaterina Krivich et al.",
  "category": "research",
  "topics": "bias-fairness,safety-alignment,children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-09T13:49:07.000Z",
  "fetched_at": "2026-07-14T16:32:15.637Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6124",
  "original_url": "https://arxiv.org/abs/2604.08263v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}