{
  "title": "Active Skillference: A Validated Prerequisite Graph, Computational Claim Registry, and SkillTree Delivery Contract",
  "version": "1.0.0",
  "doi": "10.5281/zenodo.21865643",
  "doi_url": "https://doi.org/10.5281/zenodo.21865643",
  "zenodo_record": "https://zenodo.org/records/21865643",
  "record_id": "21865644",
  "publication_date": "2026-08-10",
  "resource_type": {
    "title": "Software",
    "type": "software"
  },
  "creators": [
    {
      "name": "Friedman, Daniel Ari",
      "affiliation": "Active Inference Institute",
      "orcid": "0000-0001-6232-9096"
    }
  ],
  "description": "Active Inference and the Free Energy Principle (FEP) provide model-based accounts of belief updating, learning, and action under uncertainty. We present Active Skillference, a provenance-bound curriculum-generation and SkillTree-export system for teaching those formal ideas. The paper evaluates structural validity, quantitative provenance, citation-role coverage, and artifact reproducibility; it does not evaluate learner outcomes, establish a new theory of Active Inference, or present an intelligent tutoring system. The curriculum is expressed as code: a typed, validated directed acyclic graph of 630 skills across 111 subjects spanning all 8 strata (mathematics -> probability -> information theory -> variational methods -> the FEP -> active inference -> computation -> applications), connected by 1199 prerequisite edges with a maximum dependency depth of 75 (of which the substantive concept chain accounts for 33; the remaining depth is per-stratum review and mastery sequencing rather than conceptual prerequisite, as the methodology details). Its defining feature is content-provenance binding: every quantitative value shown to a learner is produced by a tested computational kernel and inserted through a typed claim token, never hand-typed, and the build refuses to export if a claim is unbacked or if a bare result number appears in learner prose, manuscript prose, or correct numeric quiz answers. The contribution is therefore a systems and curriculum-infrastructure artifact: it makes a formal subject inspectable and deliverable, but does not claim that the resulting path is optimal for every learner. The validated graph exports directly into SkillTree’s data model (Project -> Subjects -> Skills with learning-path dependencies and quiz-gated completion), includes a scripted REST seeding path for a configured instance, and is mirrored by a local dashboard that exposes generated artifacts, figures, claim ledgers, scholarship audits, and graph diagnostics without taking ownership of learner progress or scoring from SkillTree. The result is a curriculum with a validator-backed artifact chain: re-running the kernels regenerates the claim ledger, figures, manuscript variables, SkillTree export, and learner-facing numbers, so the platform’s teaching claims remain bounded by what the code, citations, validators, and documented limitations actually support.",
  "keywords": [
    "active inference",
    "free energy principle",
    "variational inference",
    "Bayesian inference",
    "information theory",
    "curriculum",
    "prerequisite graph",
    "SkillTree",
    "computational provenance",
    "micro-learning",
    "reproducible research"
  ],
  "files": [
    {
      "name": "Active_Skillference_v1.0.0_DOI-10.5281-zenodo.21865644.pdf",
      "size_bytes": 5731097,
      "checksum": "md5:7e43004dbef2ebc7ade2b71197b49ec5",
      "download_url": "https://zenodo.org/api/records/21865644/files/Active_Skillference_v1.0.0_DOI-10.5281-zenodo.21865644.pdf/content"
    }
  ],
  "related_resources": [],
  "github_repo": "",
  "source": "zenodo-only",
  "checked_at": "2026-08-10T17:11:39Z"
}