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Didactopus FAQ
What is Didactopus for?
Didactopus helps represent learning as a knowledge graph with evidence, mastery, artifacts, and reusable outputs. It supports both learners and the systems that author, review, and improve learning materials.
Is it only for AI learners?
No. It is built for:
- human learners
- AI learners
- hybrid workflows where AI and humans both contribute
Why emphasize synthesis?
Because understanding often improves when learners recognize structural overlap between different domains. Transfer, analogy, and conceptual reuse are central to real intellectual progress.
Examples include:
- entropy in thermodynamics and information theory
- drift in population genetics and random walks
- feedback in engineering, biology, and machine learning
Didactopus tries to surface these overlaps rather than treating subjects as sealed containers.
Why not automatically trust learner-derived knowledge?
Learner-derived knowledge can be valuable, but it still needs review, validation, and provenance. A learner may discover something surprising and useful, but the system should preserve both usefulness and caution.
What can learner-derived knowledge become?
Depending on review outcome, it can be promoted into:
- accepted pack improvements
- curriculum drafts
- reusable skill bundles
- archived but unadopted suggestions
What is the review-and-promotion workflow?
It is the process by which exported learner observations are triaged, reviewed, validated, and either promoted or archived.
What is the synthesis engine?
The synthesis engine analyzes concept graphs and learner evidence to identify candidate conceptual overlaps, analogies, and transferable structures across packs.
Can Didactopus produce traditional educational outputs?
Yes. Knowledge exports can seed:
- lesson outlines
- study guides
- exercise sets
- instructor notes
- curriculum maps
Can Didactopus produce AI skill-like outputs?
Yes. Structured exports can support:
- skill manifests
- evaluation checklists
- failure-mode notes
- canonical examples
- prerequisite maps
What happens to artifacts over time?
Artifacts can be:
- retained
- archived
- expired
- soft-deleted
Retention policy support is included so temporary debugging products and durable portfolio artifacts can be treated differently.