Overview
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.
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Methods and contributions
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Findings and contributions
- The graph connects 630 skills in 111 subjects by 1199 prerequisite edges with maximum dependency depth 75, of which the substantive concept chain accounts for 33.
- Reports a bounded engineering result: a contested formal subject can be represented as an inspectable prerequisite graph whose contracts are checked separately.
- States these are properties of the build, not evidence that the chosen order is optimal for learners or that the framework is empirically confirmed.
- Positions the release as a candidate foundation for a later adaptive study, not an adaptive system, with SkillTree owning progress and scoring.
Methods
- Typed, validated DAG curriculum of skills across 8 Active Inference strata — Encodes the curriculum as code from mathematics through FEP and active inference to applications, with prerequisite edges validated as a DAG.
- Content-provenance binding via tested kernels and typed claim tokens — Learner-facing numbers come only from tested computational kernels via claim tokens; the build refuses export on unbacked claims.
- Export to SkillTree's Project-Subject-Skill model with REST seeding — Exports the validated graph into SkillTree with learning-path dependencies and quiz-gated completion, plus a scripted REST seeding path.
- Deterministic artifact evaluation of structure, provenance, citations, reproducibility — Evaluates structural validity, quantitative provenance, citation-role coverage and artifact reproducibility, explicitly not learner outcomes.
- Kernel-backed VFE/EFE teaching examples on small discrete models — Supplement derives model-bounded examples such as noisy-sensor posteriors, EFE policy comparison, sum-product inference and Dirichlet learning.
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- Active_Skillference_v1.0.0_DOI-10.5281-zenodo.21865644.pdf PDF · 5.47 MiB · extracted-text source
Citation
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