Apply ZIP update: 275-didactopus-review-promotion-and-synthesis-engine.zip [2026-03-14T13:21:15]
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FAQ.md
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FAQ.md
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# Didactopus FAQ
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## What problem does Didactopus solve?
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## What is Didactopus for?
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Most learning systems record only test scores or completion status.
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Didactopus records the **structure of knowledge and the trajectory of learning**, allowing deeper analysis of how understanding develops.
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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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---
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## Is it only for AI learners?
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## Is Didactopus only for AI learners?
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No.
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It is designed for:
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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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- mixed human‑AI learning systems
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- hybrid workflows where AI and humans both contribute
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The architecture intentionally treats both types of learners similarly so their progress can be compared and analyzed.
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## Why emphasize synthesis?
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---
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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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## Why represent knowledge as graphs?
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Examples include:
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Knowledge graphs make relationships explicit:
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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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- prerequisites
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- conceptual similarity
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- cross‑domain analogies
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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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Graph representations make it easier to visualize and analyze the structure of understanding.
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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 is synthesis?
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## What can learner-derived knowledge become?
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Synthesis is the process of **connecting ideas across different domains**.
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Depending on review outcome, it can be promoted into:
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Many major intellectual advances come from recognizing structural similarities between fields.
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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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Didactopus supports synthesis by:
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## What is the review-and-promotion workflow?
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- allowing cross‑pack concept links
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- visualizing conceptual overlap
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- encouraging learners to explore related domains
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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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---
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## What is the synthesis engine?
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## Can learners modify domain packs?
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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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Learners can propose improvements through knowledge export artifacts.
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## Can Didactopus produce traditional educational outputs?
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These proposals may then be reviewed by:
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Yes. Knowledge exports can seed:
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- mentors
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- domain pack maintainers
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- automated validation systems
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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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Accepted improvements can be incorporated into future pack versions.
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## Can Didactopus produce AI skill-like outputs?
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Yes. Structured exports can support:
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## What are artifacts?
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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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Artifacts are outputs produced by Didactopus that capture aspects of learning.
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## What happens to artifacts over time?
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Examples:
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Artifacts can be:
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- concept graph animations
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- mastery snapshots
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- knowledge export bundles
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- research datasets
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Artifacts allow learning to be inspected, shared, and reused.
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---
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## Why have artifact retention policies?
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Learning systems can produce large numbers of artifacts.
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Retention policies allow systems to:
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- automatically remove temporary artifacts
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- preserve historically important outputs
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- archive discoveries or milestones
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---
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## Can Didactopus help build curricula?
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Yes.
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Knowledge exports and learner artifacts can be used to produce:
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- textbooks
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- course modules
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- visual learning materials
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- interactive exercises
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---
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## How does Didactopus help AI systems?
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AI agents can use domain packs as structured skill maps.
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Learner state tracking allows researchers to observe:
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- reasoning development
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- concept acquisition
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- cross‑domain transfer
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This makes Didactopus useful for studying AI learning as well as human learning.
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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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