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Didactopus
Didactopus is an AI-assisted learning and knowledge-graph platform for representing how understanding develops, how concepts relate, and how learner output can be reused to improve packs, curricula, and downstream agent skills.
It is designed for:
- human learners
- AI learners
- human/AI collaborative learning workflows
- curriculum designers
- mentors and reviewers
- researchers studying learning trajectories
The system treats learning as a graph process rather than as a sequence of isolated quiz events. Domain packs define concepts, prerequisites, and cross-pack relationships. Learner evidence updates mastery estimates and produces reusable artifacts.
Major capabilities
Domain packs
Domain packs define concept graphs, prerequisite relationships, and optional cross-pack links. Packs may be private, shared, reviewed, or published.
Learner state
Learners accumulate evidence events, mastery records, evaluation outcomes, and trajectory histories.
Animated graph views
Learning progress can be rendered as stable animated concept graphs and exported as frame bundles for GIF/MP4 production.
Artifact registry
Render bundles, knowledge exports, and derivative outputs are managed as first-class artifacts with retention metadata and lifecycle controls.
Knowledge export
Learner output can be exported as candidate structured knowledge, including:
- pack-improvement suggestions
- curriculum draft material
- skill-bundle candidates
- archived observations and discovery notes
Review and promotion workflow
Learner-derived knowledge is not treated as automatically correct. It enters a triage and review pipeline where it may be promoted into accepted Didactopus assets.
Synthesis engine
Didactopus emphasizes synthesis: discovering helpful overlaps and structural analogies between distinct topics. The synthesis engine proposes candidate links, analogy clusters, and cross-pack insights.
Philosophy
Learning as visible structure
The system should make it possible to inspect not just outcomes, but how those outcomes emerge.
Learners as discoverers
Learners sometimes find gaps, hidden prerequisites, better examples, or novel connections that mentors did not anticipate. Didactopus is designed to capture that productively.
Synthesis matters
Some of the most valuable understanding comes from linking apparently disparate topics. Didactopus explicitly supports this through:
- cross-pack links
- similarity scoring
- synthesis proposals
- reusable exports for pack revision and curriculum design
Reuse beyond Didactopus
Learner knowledge should be renderable into forms useful for:
- improved domain packs
- traditional curriculum products
- agentic AI skills
- mentor notes
- research artifacts
New additions in this update
This update adds design material for:
- review-and-promotion workflow for learner-derived knowledge
- synthesis engine architecture
- updated README and FAQ language reflecting synthesis and knowledge reuse
See:
docs/review_and_promotion_workflow.mddocs/synthesis_engine_architecture.mddocs/api_outline.mddocs/data_models.mdFAQ.md