# 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.