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