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