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Curriculum Brain: Constructing Curriculum Knowledge Graphs as a Substrate for Cognitive Diagnosis — arXiv:2610.05860

Source: arXiv
Original: https://arxiv.org/abs/2610.05860
License (as archived): CC0-1.0
Archived: 2026-10-06T05:27:12.924Z
SHA-256 of the archived text: d43ab80daa43d98fa7d6f5f06929b5e816c945c80eb12288e02d99ec9c0f5c47
Stable URL of this page: https://metraipolis.com/documento/dc_dfd83e2888b2

How to cite

Curriculum Brain: Constructing Curriculum Knowledge Graphs as a Substrate for Cognitive Diagnosis — arXiv:2610.05860 — arXiv, https://arxiv.org/abs/2610.05860 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dfd83e2888b2 (Metraipolis archive dc_dfd83e2888b2, sha256 d43ab80daa43d98fa7d6f5f06929b5e816c945c80eb12288e02d99ec9c0f5c47)

BibTeX

@misc{dcdfd83e2888b2,
  title = {Curriculum Brain: Constructing Curriculum Knowledge Graphs as a Substrate for Cognitive Diagnosis — arXiv:2610.05860},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.05860},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dfd83e2888b2; sha256 d43ab80daa43d98fa7d6f5f06929b5e816c945c80eb12288e02d99ec9c0f5c47},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.05860. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Cognitive Diagnostic Models (CDMs) identify which specific skills a student has and has not mastered, the signal a personalized learning path needs and a single aggregate score cannot give. Yet they are rarely deployed. The obstacle is their precondition: the Q-matrix, a mapping from every assessment item to the skills it requires, historically authored by hand. We separate the task into two stages: first construct the curriculum's own knowledge graph, the full space of concepts and skills it contains, independent of any item; then map items against that graph on demand. This paper addresses the first stage only. The item-mapping stage is designed but not implemented here, so the claim that this shifts judgment cost from once per item to once per curriculum is a design rationale rather than a finding. We present Curriculum Brain, a two-repository system pairing a version-controlled knowledge base with an agentic pipeline of eleven single-responsibility agents under a thin deterministic orchestrator. It generates candidate concept-skill mappings from official curriculum documents, checks them against accumulated rules, and compares them with a concept-skill map extracted independently from the textbook, repairing its own failures and escalating to a human only when it cannot resolve a case itself. Across 241 chapter runs (168 distinct chapters), 41.5% produced a Generator output passing both checks without a patch, and 67.6% resolved without escalation. Both are measured against criteria the system itself produced, so both describe internal consistency rather than agreement with an external standard, and both pool two pipeline configurations separated by a single change at run 77; after it the figures are 57.0% and 91.5%. Observed spend was $1.19 per chapter, API spend only, excluding human review. We release both the framework and the resulting curriculum dataset.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computers and Society (cs.CY) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.05860 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:24:06.078Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).