# 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.