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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.