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FAQ.md
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FAQ.md
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# Didactopus FAQ: Artifact Lifecycle and Knowledge Reuse
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## Why keep artifacts after rendering?
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# Didactopus FAQ
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Artifacts are evidence of learning trajectories, pack structure, and interpretation.
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They support:
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- learner reflection
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- mentor review
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- debugging AI learners
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- presentation and publication
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## What problem does Didactopus solve?
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## Why do retention policies matter?
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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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Not every artifact should be stored forever. Some are transient debugging outputs;
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others are durable portfolio items or research artifacts.
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---
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Retention policy support lets deployments distinguish:
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- short-lived temporary outputs
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- retained educational outputs
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- archival artifacts worth preserving
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## Is Didactopus only for AI learners?
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## How can learner knowledge be used outside Didactopus?
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No.
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A learner's activity can be exported into structured forms that support:
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- revised or expanded domain packs
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- lesson plans and conventional curriculum products
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- AI skill definitions or prompts
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- mentor-facing notes about misconceptions and discoveries
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It is designed for:
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## Can learners improve domain packs?
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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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Yes. Learners sometimes notice:
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- confusing sequence order
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- hidden prerequisites
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- missing examples
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- better analogies
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- edge cases mentors overlooked
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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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Didactopus should capture these as improvement suggestions rather than losing them.
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---
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## How could this support agentic AI skills?
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## Why represent knowledge as graphs?
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A learner knowledge export can be mapped into:
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- scope and goals
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- prerequisite structure
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- canonical examples
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- failure modes
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- evaluation checks
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- recommended actions
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Knowledge graphs make relationships explicit:
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That makes it a plausible source for building agent skills or skill-like bundles.
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- prerequisites
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- conceptual similarity
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- cross‑domain analogies
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## How could this support traditional curriculum products?
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Graph representations make it easier to visualize and analyze the structure of understanding.
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Knowledge export can seed:
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- lesson outlines
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- exercise sets
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- study guides
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- formative assessment prompts
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- instructor notes
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- capstone project ideas
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---
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## Is exported learner knowledge treated as automatically correct?
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## What is synthesis?
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Synthesis is the process of **connecting ideas across different domains**.
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Many major intellectual advances come from recognizing structural similarities between fields.
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Didactopus supports synthesis by:
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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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---
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## Can learners modify domain packs?
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Learners can propose improvements through knowledge export artifacts.
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These proposals may then be reviewed by:
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- mentors
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- domain pack maintainers
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- automated validation systems
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Accepted improvements can be incorporated into future pack versions.
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---
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## What are artifacts?
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Artifacts are outputs produced by Didactopus that capture aspects of learning.
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Examples:
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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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No. Exported learner knowledge should be treated as candidate structured knowledge.
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It is useful, but it still needs review, validation, and provenance tracking.
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