Didactopus/FAQ.md

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# Didactopus FAQ: Artifact Lifecycle and Knowledge Reuse
## Why keep artifacts after rendering?
Artifacts are evidence of learning trajectories, pack structure, and interpretation.
They support:
- learner reflection
- mentor review
- debugging AI learners
- presentation and publication
## Why do retention policies matter?
Not every artifact should be stored forever. Some are transient debugging outputs;
others are durable portfolio items or research artifacts.
Retention policy support lets deployments distinguish:
- short-lived temporary outputs
- retained educational outputs
- archival artifacts worth preserving
## How can learner knowledge be used outside Didactopus?
A learner's activity can be exported into structured forms that support:
- revised or expanded domain packs
- lesson plans and conventional curriculum products
- AI skill definitions or prompts
- mentor-facing notes about misconceptions and discoveries
## Can learners improve domain packs?
Yes. Learners sometimes notice:
- confusing sequence order
- hidden prerequisites
- missing examples
- better analogies
- edge cases mentors overlooked
Didactopus should capture these as improvement suggestions rather than losing them.
## How could this support agentic AI skills?
A learner knowledge export can be mapped into:
- scope and goals
- prerequisite structure
- canonical examples
- failure modes
- evaluation checks
- recommended actions
That makes it a plausible source for building agent skills or skill-like bundles.
## How could this support traditional curriculum products?
Knowledge export can seed:
- lesson outlines
- exercise sets
- study guides
- formative assessment prompts
- instructor notes
- capstone project ideas
## Is exported learner knowledge treated as automatically correct?
No. Exported learner knowledge should be treated as candidate structured knowledge.
It is useful, but it still needs review, validation, and provenance tracking.