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