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Didactopus

Didactopus is a local-first AI-assisted autodidactic mastery platform for building genuine expertise through concept graphs, adaptive curriculum planning, evidence-driven mastery, Socratic mentoring, and project-based learning.

Tagline: Many arms, one goal — mastery.

Complete overview to this point

Didactopus is designed to support both human learners and, potentially, agentic AI students that use the same mastery infrastructure to become competent in a target domain.

The current architecture includes:

Domain packs for contributed concepts, projects, rubrics, and mastery profiles
Dependency resolution across packs
Merged learning graph generation
Adaptive learner engine that identifies ready, blocked, and mastered concepts
Evidence engine with weighted, recency-aware, multi-dimensional mastery inference
Concept-specific mastery profiles with template inheritance
Concept graph engine for cross-pack prerequisite reasoning, concept linking, pathfinding, and graph export

Core philosophy

Didactopus assumes that real expertise is built through:

explanation
problem solving
transfer
critique
project execution

The AI layer should function as a mentor, evaluator, and curriculum partner, not an answer vending machine.

Domain packs

Knowledge enters the system through versioned, shareable domain packs. Each pack can contribute:

concepts
prerequisites
learning stages
projects
rubrics
mastery profiles
profile templates
cross-pack concept links

Concept graph engine

This revision implements a concept graph engine with:

prerequisite reasoning across packs
cross-pack concept linking
semantic concept similarity hooks
automatic curriculum pathfinding
visualization export for mastery graphs

Concepts are namespaced as pack-name::concept-id.

Cross-pack links

Domain packs may declare conceptual links such as:

equivalent_to
related_to
extends
depends_on

These links enable Didactopus to reason across pack boundaries rather than treating each pack as an isolated island.

Semantic similarity

A semantic similarity layer is included as a hook for:

token overlap similarity
future embedding-based similarity
future ontology and LLM-assisted concept alignment

Curriculum pathfinding

The concept graph engine supports:

prerequisite chains
shortest dependency paths
next-ready concept discovery
reachability analysis
curriculum path generation from a learner’s mastery state to a target concept

Visualization

Graphs can be exported to:

Graphviz DOT
Cytoscape-style JSON

Evidence-driven mastery

Mastery is inferred from evidence such as:

explanations
problem solutions
transfer tasks
project artifacts

Evidence is:

weighted by type
optionally up-weighted for recency
summarized by competence dimension
compared against concept-specific mastery profiles

Multi-dimensional mastery

Current dimensions include:

correctness
explanation
transfer
project_execution
critique

Different concepts can require different subsets of these dimensions.

Agentic AI students

Didactopus is also architecturally suitable for AI learner agents.

An agentic AI student could:

ingest domain packs
traverse the concept graph
generate explanations and answers
attempt practice tasks
critique model outputs
complete simulated projects
accumulate evidence
advance only when concept-specific mastery criteria are satisfied

Repository structure

didactopus/
├── README.md
├── artwork/
├── configs/
├── docs/
├── domain-packs/
├── src/didactopus/
└── tests/

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