204 lines
5.2 KiB
Markdown
204 lines
5.2 KiB
Markdown
# Didactopus
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**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.
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**Tagline:** *Many arms, one goal — mastery.*
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## This revision
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This revision adds a **graph-aware planning layer** that connects the concept graph engine to the adaptive and evidence engines.
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The new planner selects the next concepts to study using a utility function that considers:
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- prerequisite readiness
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- distance to learner target concepts
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- weakness in competence dimensions
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- project availability
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- review priority for fragile concepts
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- semantic neighborhood around learner goals
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## Why this matters
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Up to this point, Didactopus could:
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- build concept graphs
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- identify ready concepts
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- infer mastery from evidence
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But it still needed a better mechanism for choosing **what to do next**.
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The graph-aware planner begins to solve that by ranking candidate concepts according to learner-specific utility instead of using unlocked prerequisites alone.
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## Current architecture overview
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Didactopus now includes:
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- **Domain packs** for concepts, projects, rubrics, mastery profiles, templates, and cross-pack links
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- **Dependency resolution** across packs
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- **Merged learning graph** generation
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- **Concept graph engine** with cross-pack links, similarity hooks, pathfinding, and visualization export
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- **Adaptive learner engine** for ready/blocked/mastered concept states
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- **Evidence engine** with weighted, recency-aware, multi-dimensional mastery inference
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- **Concept-specific mastery profiles** with template inheritance
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- **Graph-aware planner** for utility-ranked next-step recommendations
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## Planning utility
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The current planner computes a score per candidate concept using:
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- readiness bonus
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- target-distance bonus
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- weak-dimension bonus
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- fragile-concept review bonus
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- project-unlock bonus
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- semantic-similarity bonus
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These terms are transparent and configurable.
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## Agentic AI students
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This planner also strengthens the case for **AI student agents** that use Didactopus as a structured mastery environment.
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An AI student could:
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1. inspect the graph
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2. choose the next concept via the planner
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3. attempt tasks
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4. generate evidence
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5. update mastery state
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6. repeat until a target expertise profile is reached
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This makes Didactopus useful as both:
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- a learning platform
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- a benchmark harness for agentic expertise growth
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## Core philosophy
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Didactopus assumes that real expertise is built through:
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- explanation
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- problem solving
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- transfer
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- critique
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- project execution
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The AI layer should function as a **mentor, evaluator, and curriculum partner**, not an answer vending machine.
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## Domain packs
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Knowledge enters the system through versioned, shareable **domain packs**. Each pack can contribute:
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- concepts
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- prerequisites
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- learning stages
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- projects
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- rubrics
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- mastery profiles
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- profile templates
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- cross-pack concept links
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## Concept graph engine
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This revision implements a concept graph engine with:
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- prerequisite reasoning across packs
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- cross-pack concept linking
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- semantic concept similarity hooks
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- automatic curriculum pathfinding
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- visualization export for mastery graphs
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Concepts are namespaced as `pack-name::concept-id`.
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### Cross-pack links
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Domain packs may declare conceptual links such as:
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- `equivalent_to`
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- `related_to`
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- `extends`
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- `depends_on`
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These links enable Didactopus to reason across pack boundaries rather than treating each pack as an isolated island.
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### Semantic similarity
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A semantic similarity layer is included as a hook for:
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- token overlap similarity
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- future embedding-based similarity
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- future ontology and LLM-assisted concept alignment
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### Curriculum pathfinding
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The concept graph engine supports:
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- prerequisite chains
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- shortest dependency paths
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- next-ready concept discovery
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- reachability analysis
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- curriculum path generation from a learner’s mastery state to a target concept
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### Visualization
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Graphs can be exported to:
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- Graphviz DOT
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- Cytoscape-style JSON
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## Evidence-driven mastery
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Mastery is inferred from evidence such as:
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- explanations
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- problem solutions
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- transfer tasks
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- project artifacts
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Evidence is:
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- weighted by type
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- optionally up-weighted for recency
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- summarized by competence dimension
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- compared against concept-specific mastery profiles
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## Multi-dimensional mastery
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Current dimensions include:
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- `correctness`
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- `explanation`
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- `transfer`
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- `project_execution`
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- `critique`
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Different concepts can require different subsets of these dimensions.
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## Agentic AI students
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Didactopus is also architecturally suitable for **AI learner agents**.
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An agentic AI student could:
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1. ingest domain packs
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2. traverse the concept graph
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3. generate explanations and answers
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4. attempt practice tasks
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5. critique model outputs
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6. complete simulated projects
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7. accumulate evidence
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8. advance only when concept-specific mastery criteria are satisfied
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## Repository structure
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```text
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didactopus/
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├── README.md
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├── artwork/
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├── configs/
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├── docs/
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├── domain-packs/
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├── src/didactopus/
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└── tests/
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```
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