Concept graph and learner updates.
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README.md
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README.md
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# Didactopus
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**Didactopus** is a local‑first AI‑assisted autodidactic mastery platform designed to help motivated learners achieve **true expertise** in a chosen domain.
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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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The system combines:
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## Complete overview to this point
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• domain knowledge graphs
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• mastery‑based learning models
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• evidence‑driven assessment
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• Socratic mentoring
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• adaptive curriculum generation
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• project‑based evaluation
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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.
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Didactopus is designed for **serious learning**, not shallow answer generation.
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The current architecture includes:
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Its core philosophy is:
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- **Domain packs** for contributed concepts, projects, rubrics, and mastery profiles
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- **Dependency resolution** across packs
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- **Merged learning graph** generation
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- **Adaptive learner engine** that identifies ready, blocked, and mastered concepts
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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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- **Concept graph engine** for cross-pack prerequisite reasoning, concept linking, pathfinding, and graph export
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> AI should function as a mentor, evaluator, and guide — not a substitute for thinking.
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## Core philosophy
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---
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Didactopus assumes that real expertise is built through:
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# Project Goals
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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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Didactopus aims to enable learners to:
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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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• build deep conceptual understanding
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• practice reasoning and explanation
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• complete real projects demonstrating competence
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• identify weak areas through evidence‑based feedback
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• progress through mastery rather than time spent
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## Domain packs
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The platform is particularly suitable for:
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Knowledge enters the system through versioned, shareable **domain packs**. Each pack can contribute:
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• autodidacts
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• researchers entering new fields
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• students supplementing formal education
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• interdisciplinary learners
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• AI‑assisted self‑study programs
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---
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# Key Architectural Concepts
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## Domain Packs
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Knowledge is distributed as **domain packs** contributed by the community.
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Each pack can include:
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- concept definitions
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- prerequisite graphs
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- learning roadmaps
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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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Example packs:
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## Concept graph engine
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```
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domain-packs/
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statistics-foundations
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bayes-extension
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applied-inference
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```
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This revision implements a concept graph engine with:
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Domain packs are validated, dependency‑checked, and merged into a **unified learning graph**.
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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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---
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Concepts are namespaced as `pack-name::concept-id`.
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# Learning Graph
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### Cross-pack links
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Didactopus merges all installed packs into a directed concept graph:
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Domain packs may declare conceptual links such as:
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```
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Concept A → Concept B → Concept C
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```
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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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Edges represent prerequisites.
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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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The system then generates:
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### Semantic similarity
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• adaptive learning roadmaps
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• next-best concepts to study
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• projects unlocked by prerequisite completion
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A semantic similarity layer is included as a hook for:
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---
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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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# Evidence‑Driven Mastery
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### Curriculum pathfinding
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Concept mastery is **inferred from evidence**, not declared.
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The concept graph engine supports:
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Evidence types include:
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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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• explanations
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• problem solutions
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• transfer tasks
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• project deliverables
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### Visualization
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Evidence contributes weighted scores that determine:
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Graphs can be exported to:
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• mastery state
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• learner confidence
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• weak dimensions requiring further practice
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- Graphviz DOT
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- Cytoscape-style JSON
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---
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## Evidence-driven mastery
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# Multi‑Dimensional Mastery
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Mastery is inferred from evidence such as:
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Didactopus tracks multiple competence dimensions:
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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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| Dimension | Meaning |
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|---|---|
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| correctness | accurate reasoning |
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| explanation | ability to explain clearly |
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| transfer | ability to apply knowledge |
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| project_execution | ability to build artifacts |
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| critique | ability to detect errors and assumptions |
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Evidence is:
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Different concepts can require different combinations of these dimensions.
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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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---
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## Multi-dimensional mastery
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# Concept Mastery Profiles
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Current dimensions include:
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Concepts define **mastery profiles** specifying:
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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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• required dimensions
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• threshold overrides
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Different concepts can require different subsets of these dimensions.
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Example:
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## Agentic AI students
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```yaml
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mastery_profile:
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required_dimensions:
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- correctness
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- transfer
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- critique
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dimension_threshold_overrides:
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transfer: 0.8
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critique: 0.8
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```
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Didactopus is also architecturally suitable for **AI learner agents**.
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---
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An agentic AI student could:
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# Mastery Profile Inheritance
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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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This revision adds **profile templates** so packs can define reusable mastery models.
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## Repository structure
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Example:
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```yaml
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profile_templates:
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foundation_concept:
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required_dimensions:
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- correctness
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- explanation
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critique_concept:
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required_dimensions:
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- correctness
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- transfer
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- critique
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```
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Concepts can reference templates:
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```yaml
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mastery_profile:
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template: critique_concept
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```
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This allows domain packs to remain concise while maintaining consistent evaluation standards.
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---
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# Adaptive Learning Engine
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The adaptive engine computes:
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• which concepts are ready to study
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• which are blocked by prerequisites
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• which are already mastered
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• which projects are available
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Output includes:
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```
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next_best_concepts
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eligible_projects
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adaptive_learning_roadmap
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```
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---
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# Evidence Engine
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The evidence engine:
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• aggregates learner evidence
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• computes weighted scores
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• tracks confidence
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• identifies weak competence dimensions
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• updates mastery status
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Later weak performance can **resurface concepts for review**.
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---
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# Socratic Mentor
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Didactopus includes a mentor layer that:
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• asks probing questions
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• challenges reasoning
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• generates practice tasks
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• proposes projects
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Models can run locally (recommended) or via remote APIs.
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---
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# Agentic AI Students
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Didactopus is also suitable for **AI‑driven learning agents**.
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A future architecture may include:
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```
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Didactopus Core
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│
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├─ Human Learner
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└─ AI Student Agent
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```
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An AI student could:
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1. read domain packs
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2. attempt practice tasks
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3. produce explanations
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4. critique model outputs
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5. complete simulated projects
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6. accumulate evidence
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7. progress through the mastery graph
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Such agents could be used for:
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• automated curriculum testing
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• benchmarking AI reasoning
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• synthetic expert generation
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• evaluation of model capabilities
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Didactopus therefore supports both:
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• human learners
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• agentic AI learners
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---
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# Project Structure
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```
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```text
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didactopus/
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adaptive_engine/
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artifact_registry/
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evidence_engine/
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learning_graph/
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mentor/
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practice/
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project_advisor/
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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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Additional directories:
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```
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configs/
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docs/
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domain-packs/
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tests/
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artwork/
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```
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---
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# Current Status
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Implemented:
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✓ domain pack validation
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✓ dependency resolution
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✓ learning graph merge
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✓ adaptive roadmap generation
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✓ evidence‑driven mastery
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✓ multi‑dimensional competence tracking
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✓ concept‑specific mastery profiles
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✓ profile template inheritance
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Planned next phases:
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• curriculum optimization algorithms
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• active‑learning task generation
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• automated project evaluation
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• distributed pack registry
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• visualization tools for learning graphs
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---
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# Philosophy
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Didactopus is built around a simple principle:
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> Mastery requires thinking, explaining, testing, and building — not merely receiving answers.
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AI can accelerate the process, but genuine learning remains an **active intellectual endeavor**.
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---
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**Didactopus — many arms, one goal: mastery.**
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@ -4,25 +4,9 @@ model_provider:
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backend: ollama
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endpoint: http://localhost:11434
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model_name: llama3.1:8b
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remote:
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enabled: false
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provider_name: none
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endpoint: ""
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model_name: ""
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platform:
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verification_required: true
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require_learner_explanations: true
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permit_direct_answers: false
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resurfacing_threshold: 0.55
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confidence_threshold: 0.8
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evidence_weights:
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explanation: 1.0
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problem: 1.5
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project: 2.5
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transfer: 2.0
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recent_evidence_multiplier: 1.35
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dimension_thresholds:
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default_dimension_thresholds:
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correctness: 0.8
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explanation: 0.75
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transfer: 0.7
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@ -32,4 +16,3 @@ platform:
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artifacts:
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local_pack_dirs:
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- domain-packs
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allow_third_party_packs: true
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# Concept Graph Engine
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The concept graph engine provides the backbone for Didactopus.
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## Features in this revision
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- prerequisite reasoning across packs
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- cross-pack concept linking
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- semantic similarity scoring hook
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- curriculum pathfinding
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- visualization export
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## Edge types
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The engine distinguishes between:
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- `prerequisite`
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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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Only prerequisite edges are used for strict learning-order pathfinding.
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concepts:
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- id: model-checking
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title: Model Checking
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description: Critiquing assumptions, fit, and implications of a probabilistic model.
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prerequisites: []
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mastery_signals:
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- compare model assumptions
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- critique a simple inference model
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mastery_profile:
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template: critique_heavy
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@ -4,7 +4,7 @@ version: 0.2.0
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schema_version: "1"
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didactopus_min_version: 0.1.0
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didactopus_max_version: 0.9.99
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description: Simple applied inference pack.
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description: Applied inference pack emphasizing transfer and critique.
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author: Wesley R. Elsberry
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license: MIT
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dependencies:
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@ -12,3 +12,19 @@ dependencies:
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min_version: 0.1.0
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max_version: 1.0.0
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overrides: []
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profile_templates:
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critique_heavy:
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required_dimensions:
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- correctness
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- transfer
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- critique
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dimension_threshold_overrides:
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transfer: 0.78
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critique: 0.73
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cross_pack_links:
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- source_concept: model-checking
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target_concept: bayes-extension::posterior
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relation: extends
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- source_concept: model-checking
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target_concept: foundations-statistics::probability-basics
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relation: related_to
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concepts:
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- id: prior
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title: Prior
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description: A probability distribution representing knowledge before evidence.
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prerequisites: []
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mastery_signals:
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- explain a prior distribution
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- compare reasonable priors
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mastery_profile:
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template: bayes_concept
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- id: posterior
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title: Posterior
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description: Updated beliefs after conditioning on observed evidence.
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prerequisites:
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- prior
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mastery_signals:
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- explain updating beliefs
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- compare prior and posterior distributions
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mastery_profile:
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required_dimensions:
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- correctness
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- explanation
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- transfer
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- critique
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dimension_threshold_overrides:
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critique: 0.78
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|
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min_version: 1.0.0
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max_version: 1.9.99
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overrides: []
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profile_templates:
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bayes_concept:
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required_dimensions:
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- correctness
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- explanation
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- transfer
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dimension_threshold_overrides:
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transfer: 0.74
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cross_pack_links:
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- source_concept: prior
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target_concept: foundations-statistics::probability-basics
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relation: depends_on
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- source_concept: posterior
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target_concept: applied-inference::model-checking
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relation: related_to
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|
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concepts:
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- id: descriptive-statistics
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title: Descriptive Statistics
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description: Core summaries of distributions, central tendency, and spread.
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prerequisites: []
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mastery_signals:
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- explain central tendency
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- explain mean median and variance
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- summarize a simple dataset
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mastery_profile:
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template: foundation_concept
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- id: probability-basics
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title: Probability Basics
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description: Basic event probability and conditional probability.
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prerequisites:
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- descriptive-statistics
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mastery_signals:
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- explain event probability
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- calculate simple conditional probability
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mastery_profile:
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required_dimensions:
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- correctness
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- explanation
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- transfer
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dimension_threshold_overrides:
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transfer: 0.72
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|
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|
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@ -9,3 +9,11 @@ author: Wesley R. Elsberry
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license: MIT
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dependencies: []
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overrides: []
|
||||
profile_templates:
|
||||
foundation_concept:
|
||||
required_dimensions:
|
||||
- correctness
|
||||
- explanation
|
||||
dimension_threshold_overrides:
|
||||
explanation: 0.75
|
||||
cross_pack_links: []
|
||||
|
|
|
|||
|
|
@ -10,8 +10,11 @@ readme = "README.md"
|
|||
requires-python = ">=3.10"
|
||||
license = {text = "MIT"}
|
||||
authors = [{name = "Wesley R. Elsberry"}]
|
||||
dependencies = ["pydantic>=2.7", "pyyaml>=6.0", "networkx>=3.2"]
|
||||
|
||||
dependencies = [
|
||||
"pydantic>=2.7",
|
||||
"pyyaml>=6.0",
|
||||
"networkx>=3.2",
|
||||
]
|
||||
[project.optional-dependencies]
|
||||
dev = ["pytest>=8.0", "ruff>=0.6"]
|
||||
|
||||
|
|
|
|||
|
|
@ -46,25 +46,32 @@ def validate_pack(pack_dir: str | Path) -> PackValidationResult:
|
|||
result.errors.append(f"missing required file: {filename}")
|
||||
if result.errors:
|
||||
return result
|
||||
|
||||
try:
|
||||
result.manifest = PackManifest.model_validate(_load_yaml(pack_path / "pack.yaml"))
|
||||
if not _version_in_range(DIDACTOPUS_VERSION, result.manifest.didactopus_min_version, result.manifest.didactopus_max_version):
|
||||
if not _version_in_range(
|
||||
DIDACTOPUS_VERSION,
|
||||
result.manifest.didactopus_min_version,
|
||||
result.manifest.didactopus_max_version,
|
||||
):
|
||||
result.errors.append(
|
||||
f"incompatible with Didactopus core version {DIDACTOPUS_VERSION}; supported range is "
|
||||
f"{result.manifest.didactopus_min_version}..{result.manifest.didactopus_max_version}"
|
||||
f"incompatible with Didactopus core version {DIDACTOPUS_VERSION}; "
|
||||
f"supported range is {result.manifest.didactopus_min_version}..{result.manifest.didactopus_max_version}"
|
||||
)
|
||||
|
||||
result.loaded_files["concepts"] = ConceptsFile.model_validate(_load_yaml(pack_path / "concepts.yaml"))
|
||||
result.loaded_files["roadmap"] = RoadmapFile.model_validate(_load_yaml(pack_path / "roadmap.yaml"))
|
||||
result.loaded_files["projects"] = ProjectsFile.model_validate(_load_yaml(pack_path / "projects.yaml"))
|
||||
result.loaded_files["rubrics"] = RubricsFile.model_validate(_load_yaml(pack_path / "rubrics.yaml"))
|
||||
except Exception as exc:
|
||||
result.errors.append(str(exc))
|
||||
|
||||
result.is_valid = not result.errors
|
||||
return result
|
||||
|
||||
|
||||
def discover_domain_packs(base_dirs: list[str | Path]) -> list[PackValidationResult]:
|
||||
results = []
|
||||
results: list[PackValidationResult] = []
|
||||
for base_dir in base_dirs:
|
||||
base = Path(base_dir)
|
||||
if not base.exists():
|
||||
|
|
@ -75,7 +82,7 @@ def discover_domain_packs(base_dirs: list[str | Path]) -> list[PackValidationRes
|
|||
|
||||
|
||||
def check_pack_dependencies(results: list[PackValidationResult]) -> list[str]:
|
||||
errors = []
|
||||
errors: list[str] = []
|
||||
manifest_by_name = {r.manifest.name: r.manifest for r in results if r.manifest is not None}
|
||||
for result in results:
|
||||
if result.manifest is None:
|
||||
|
|
|
|||
|
|
@ -8,6 +8,23 @@ class DependencySpec(BaseModel):
|
|||
max_version: str = "9999.9999.9999"
|
||||
|
||||
|
||||
class MasteryProfileSpec(BaseModel):
|
||||
template: str | None = None
|
||||
required_dimensions: list[str] = Field(default_factory=list)
|
||||
dimension_threshold_overrides: dict[str, float] = Field(default_factory=dict)
|
||||
|
||||
|
||||
class CrossPackLinkSpec(BaseModel):
|
||||
source_concept: str
|
||||
target_concept: str
|
||||
relation: str
|
||||
|
||||
|
||||
class ProfileTemplateSpec(BaseModel):
|
||||
required_dimensions: list[str] = Field(default_factory=list)
|
||||
dimension_threshold_overrides: dict[str, float] = Field(default_factory=dict)
|
||||
|
||||
|
||||
class PackManifest(BaseModel):
|
||||
name: str
|
||||
display_name: str
|
||||
|
|
@ -20,13 +37,17 @@ class PackManifest(BaseModel):
|
|||
license: str = "unspecified"
|
||||
dependencies: list[DependencySpec] = Field(default_factory=list)
|
||||
overrides: list[str] = Field(default_factory=list)
|
||||
profile_templates: dict[str, ProfileTemplateSpec] = Field(default_factory=dict)
|
||||
cross_pack_links: list[CrossPackLinkSpec] = Field(default_factory=list)
|
||||
|
||||
|
||||
class ConceptEntry(BaseModel):
|
||||
id: str
|
||||
title: str
|
||||
description: str = ""
|
||||
prerequisites: list[str] = Field(default_factory=list)
|
||||
mastery_signals: list[str] = Field(default_factory=list)
|
||||
mastery_profile: MasteryProfileSpec = Field(default_factory=MasteryProfileSpec)
|
||||
|
||||
|
||||
class ConceptsFile(BaseModel):
|
||||
|
|
|
|||
|
|
@ -0,0 +1,93 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any
|
||||
import json
|
||||
import networkx as nx
|
||||
|
||||
REL_PREREQ = "prerequisite"
|
||||
REL_EQUIVALENT = "equivalent_to"
|
||||
REL_RELATED = "related_to"
|
||||
REL_EXTENDS = "extends"
|
||||
REL_DEPENDS = "depends_on"
|
||||
|
||||
|
||||
@dataclass
|
||||
class ConceptGraph:
|
||||
graph: nx.MultiDiGraph = field(default_factory=nx.MultiDiGraph)
|
||||
|
||||
def add_concept(self, concept_key: str, metadata: dict[str, Any] | None = None) -> None:
|
||||
self.graph.add_node(concept_key, **(metadata or {}))
|
||||
|
||||
def add_edge(self, source: str, target: str, relation: str) -> None:
|
||||
self.graph.add_edge(source, target, relation=relation)
|
||||
|
||||
def add_prerequisite(self, prereq: str, concept: str) -> None:
|
||||
self.add_edge(prereq, concept, REL_PREREQ)
|
||||
|
||||
def add_cross_link(self, source: str, target: str, relation: str) -> None:
|
||||
self.add_edge(source, target, relation)
|
||||
|
||||
def prerequisite_subgraph(self) -> nx.DiGraph:
|
||||
g = nx.DiGraph()
|
||||
for node, data in self.graph.nodes(data=True):
|
||||
g.add_node(node, **data)
|
||||
for u, v, data in self.graph.edges(data=True):
|
||||
if data.get("relation") == REL_PREREQ:
|
||||
g.add_edge(u, v)
|
||||
return g
|
||||
|
||||
def prerequisites(self, concept: str) -> list[str]:
|
||||
return list(self.prerequisite_subgraph().predecessors(concept))
|
||||
|
||||
def prerequisite_chain(self, concept: str) -> list[str]:
|
||||
return list(nx.ancestors(self.prerequisite_subgraph(), concept))
|
||||
|
||||
def dependents(self, concept: str) -> list[str]:
|
||||
return list(self.prerequisite_subgraph().successors(concept))
|
||||
|
||||
def learning_path(self, start: str, target: str) -> list[str] | None:
|
||||
try:
|
||||
return nx.shortest_path(self.prerequisite_subgraph(), start, target)
|
||||
except nx.NetworkXNoPath:
|
||||
return None
|
||||
|
||||
def curriculum_path_to_target(self, mastered: set[str], target: str) -> list[str]:
|
||||
pg = self.prerequisite_subgraph()
|
||||
needed = set(nx.ancestors(pg, target)) | {target}
|
||||
ordered = [n for n in nx.topological_sort(pg) if n in needed]
|
||||
return [n for n in ordered if n not in mastered]
|
||||
|
||||
def ready_concepts(self, mastered: set[str]) -> list[str]:
|
||||
pg = self.prerequisite_subgraph()
|
||||
ready = []
|
||||
for node in pg.nodes:
|
||||
if node in mastered:
|
||||
continue
|
||||
if set(pg.predecessors(node)).issubset(mastered):
|
||||
ready.append(node)
|
||||
return ready
|
||||
|
||||
def related_concepts(self, concept: str, relation_types: set[str] | None = None) -> list[str]:
|
||||
relation_types = relation_types or {REL_EQUIVALENT, REL_RELATED, REL_EXTENDS, REL_DEPENDS}
|
||||
found = []
|
||||
for _, v, data in self.graph.out_edges(concept, data=True):
|
||||
if data.get("relation") in relation_types:
|
||||
found.append(v)
|
||||
return found
|
||||
|
||||
def export_graphviz(self, path: str) -> None:
|
||||
lines = ["digraph Didactopus {"]
|
||||
for node in self.graph.nodes:
|
||||
lines.append(f' "{node}";')
|
||||
for u, v, data in self.graph.edges(data=True):
|
||||
lines.append(f' "{u}" -> "{v}" [label="{data.get("relation", "")}"];')
|
||||
lines.append("}")
|
||||
Path(path).write_text("\n".join(lines), encoding="utf-8")
|
||||
|
||||
def export_cytoscape_json(self, path: str) -> None:
|
||||
data = {
|
||||
"nodes": [{"data": {"id": n, **attrs}} for n, attrs in self.graph.nodes(data=True)],
|
||||
"edges": [{"data": {"source": u, "target": v, **attrs}} for u, v, attrs in self.graph.edges(data=True)],
|
||||
}
|
||||
Path(path).write_text(json.dumps(data, indent=2), encoding="utf-8")
|
||||
|
|
@ -9,35 +9,13 @@ class ProviderEndpoint(BaseModel):
|
|||
model_name: str = "llama3.1:8b"
|
||||
|
||||
|
||||
class RemoteProvider(BaseModel):
|
||||
enabled: bool = False
|
||||
provider_name: str = "none"
|
||||
endpoint: str = ""
|
||||
model_name: str = ""
|
||||
|
||||
|
||||
class ModelProviderConfig(BaseModel):
|
||||
mode: str = Field(default="local_first")
|
||||
local: ProviderEndpoint = Field(default_factory=ProviderEndpoint)
|
||||
remote: RemoteProvider = Field(default_factory=RemoteProvider)
|
||||
|
||||
|
||||
class PlatformConfig(BaseModel):
|
||||
verification_required: bool = True
|
||||
require_learner_explanations: bool = True
|
||||
permit_direct_answers: bool = False
|
||||
resurfacing_threshold: float = 0.55
|
||||
confidence_threshold: float = 0.8
|
||||
evidence_weights: dict[str, float] = Field(
|
||||
default_factory=lambda: {
|
||||
"explanation": 1.0,
|
||||
"problem": 1.5,
|
||||
"project": 2.5,
|
||||
"transfer": 2.0,
|
||||
}
|
||||
)
|
||||
recent_evidence_multiplier: float = 1.35
|
||||
dimension_thresholds: dict[str, float] = Field(
|
||||
default_dimension_thresholds: dict[str, float] = Field(
|
||||
default_factory=lambda: {
|
||||
"correctness": 0.8,
|
||||
"explanation": 0.75,
|
||||
|
|
@ -50,7 +28,6 @@ class PlatformConfig(BaseModel):
|
|||
|
||||
class ArtifactConfig(BaseModel):
|
||||
local_pack_dirs: list[str] = Field(default_factory=lambda: ["domain-packs"])
|
||||
allow_third_party_packs: bool = True
|
||||
|
||||
|
||||
class AppConfig(BaseModel):
|
||||
|
|
|
|||
|
|
@ -0,0 +1,49 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from .artifact_registry import PackValidationResult
|
||||
from .concept_graph import ConceptGraph
|
||||
from .learning_graph import build_merged_learning_graph, namespaced_concept
|
||||
from .semantic_similarity import concept_similarity
|
||||
|
||||
|
||||
def build_concept_graph(
|
||||
results: list[PackValidationResult],
|
||||
default_dimension_thresholds: dict[str, float],
|
||||
) -> ConceptGraph:
|
||||
merged = build_merged_learning_graph(results, default_dimension_thresholds)
|
||||
|
||||
graph = ConceptGraph()
|
||||
for concept_key, data in merged.concept_data.items():
|
||||
graph.add_concept(concept_key, data)
|
||||
|
||||
for concept_key, data in merged.concept_data.items():
|
||||
for prereq in data["prerequisites"]:
|
||||
if prereq in merged.concept_data:
|
||||
graph.add_prerequisite(prereq, concept_key)
|
||||
|
||||
for result in results:
|
||||
if result.manifest is None or not result.is_valid:
|
||||
continue
|
||||
pack_name = result.manifest.name
|
||||
for link in result.manifest.cross_pack_links:
|
||||
source = link.source_concept if "::" in link.source_concept else namespaced_concept(pack_name, link.source_concept)
|
||||
target = link.target_concept
|
||||
if source in graph.graph.nodes and target in graph.graph.nodes:
|
||||
graph.add_cross_link(source, target, link.relation)
|
||||
|
||||
return graph
|
||||
|
||||
|
||||
def suggest_semantic_links(graph: ConceptGraph, minimum_similarity: float = 0.35) -> list[tuple[str, str, float]]:
|
||||
concepts = list(graph.graph.nodes(data=True))
|
||||
found = []
|
||||
for i in range(len(concepts)):
|
||||
key_a, data_a = concepts[i]
|
||||
for j in range(i + 1, len(concepts)):
|
||||
key_b, data_b = concepts[j]
|
||||
if key_a.split("::")[0] == key_b.split("::")[0]:
|
||||
continue
|
||||
sim = concept_similarity(data_a, data_b)
|
||||
if sim >= minimum_similarity:
|
||||
found.append((key_a, key_b, sim))
|
||||
return sorted(found, key=lambda x: x[2], reverse=True)
|
||||
|
|
@ -2,9 +2,9 @@ from __future__ import annotations
|
|||
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any
|
||||
import networkx as nx
|
||||
|
||||
from .artifact_registry import PackValidationResult, topological_pack_order
|
||||
from .profile_templates import resolve_mastery_profile
|
||||
|
||||
|
||||
def namespaced_concept(pack_name: str, concept_id: str) -> str:
|
||||
|
|
@ -13,38 +13,44 @@ def namespaced_concept(pack_name: str, concept_id: str) -> str:
|
|||
|
||||
@dataclass
|
||||
class MergedLearningGraph:
|
||||
graph: nx.DiGraph = field(default_factory=nx.DiGraph)
|
||||
concept_data: dict[str, dict[str, Any]] = field(default_factory=dict)
|
||||
project_catalog: list[dict[str, Any]] = field(default_factory=list)
|
||||
load_order: list[str] = field(default_factory=list)
|
||||
|
||||
|
||||
def build_merged_learning_graph(results: list[PackValidationResult]) -> MergedLearningGraph:
|
||||
def build_merged_learning_graph(
|
||||
results: list[PackValidationResult],
|
||||
default_dimension_thresholds: dict[str, float],
|
||||
) -> MergedLearningGraph:
|
||||
merged = MergedLearningGraph()
|
||||
valid = {r.manifest.name: r for r in results if r.manifest is not None and r.is_valid}
|
||||
merged.load_order = topological_pack_order(results)
|
||||
|
||||
for pack_name in merged.load_order:
|
||||
result = valid[pack_name]
|
||||
templates = {
|
||||
name: {
|
||||
"required_dimensions": list(spec.required_dimensions),
|
||||
"dimension_threshold_overrides": dict(spec.dimension_threshold_overrides),
|
||||
}
|
||||
for name, spec in result.manifest.profile_templates.items()
|
||||
}
|
||||
for concept in result.loaded_files["concepts"].concepts:
|
||||
key = namespaced_concept(pack_name, concept.id)
|
||||
resolved_profile = resolve_mastery_profile(
|
||||
concept.mastery_profile.model_dump(),
|
||||
templates,
|
||||
default_dimension_thresholds,
|
||||
)
|
||||
merged.concept_data[key] = {
|
||||
"id": concept.id,
|
||||
"title": concept.title,
|
||||
"description": concept.description,
|
||||
"pack": pack_name,
|
||||
"prerequisites": list(concept.prerequisites),
|
||||
"prerequisites": [namespaced_concept(pack_name, p) for p in concept.prerequisites],
|
||||
"mastery_signals": list(concept.mastery_signals),
|
||||
"mastery_profile": resolved_profile,
|
||||
}
|
||||
merged.graph.add_node(key)
|
||||
|
||||
for pack_name in merged.load_order:
|
||||
result = valid[pack_name]
|
||||
for concept in result.loaded_files["concepts"].concepts:
|
||||
concept_key = namespaced_concept(pack_name, concept.id)
|
||||
for prereq in concept.prerequisites:
|
||||
prereq_key = namespaced_concept(pack_name, prereq)
|
||||
if prereq_key in merged.graph:
|
||||
merged.graph.add_edge(prereq_key, concept_key)
|
||||
for project in result.loaded_files["projects"].projects:
|
||||
merged.project_catalog.append({
|
||||
"id": f"{pack_name}::{project.id}",
|
||||
|
|
|
|||
|
|
@ -2,141 +2,73 @@ import argparse
|
|||
import os
|
||||
from pathlib import Path
|
||||
|
||||
from .adaptive_engine import LearnerProfile, build_adaptive_plan
|
||||
from .artifact_registry import (
|
||||
check_pack_dependencies,
|
||||
detect_dependency_cycles,
|
||||
discover_domain_packs,
|
||||
topological_pack_order,
|
||||
)
|
||||
from .artifact_registry import check_pack_dependencies, detect_dependency_cycles, discover_domain_packs
|
||||
from .config import load_config
|
||||
from .evidence_engine import EvidenceItem, ingest_evidence_bundle
|
||||
from .learning_graph import build_merged_learning_graph
|
||||
from .mentor import generate_socratic_prompt
|
||||
from .model_provider import ModelProvider
|
||||
from .practice import generate_practice_task
|
||||
from .project_advisor import suggest_capstone
|
||||
from .graph_builder import build_concept_graph, suggest_semantic_links
|
||||
|
||||
|
||||
def build_parser() -> argparse.ArgumentParser:
|
||||
parser = argparse.ArgumentParser(description="Didactopus multi-dimensional mastery scaffold")
|
||||
parser.add_argument("--domain", required=True)
|
||||
parser.add_argument("--goal", required=True)
|
||||
parser.add_argument(
|
||||
"--config",
|
||||
default=os.environ.get("DIDACTOPUS_CONFIG", "configs/config.example.yaml"),
|
||||
)
|
||||
parser = argparse.ArgumentParser(description="Didactopus concept graph engine")
|
||||
parser.add_argument("--target", default="bayes-extension::posterior")
|
||||
parser.add_argument("--mastered", nargs="*", default=[])
|
||||
parser.add_argument("--export-dot", default="")
|
||||
parser.add_argument("--export-cytoscape", default="")
|
||||
parser.add_argument("--config", default=os.environ.get("DIDACTOPUS_CONFIG", "configs/config.example.yaml"))
|
||||
return parser
|
||||
|
||||
|
||||
def main() -> None:
|
||||
args = build_parser().parse_args()
|
||||
config = load_config(Path(args.config))
|
||||
provider = ModelProvider(config.model_provider)
|
||||
packs = discover_domain_packs(config.artifacts.local_pack_dirs)
|
||||
dependency_errors = check_pack_dependencies(packs)
|
||||
cycles = detect_dependency_cycles(packs)
|
||||
results = discover_domain_packs(config.artifacts.local_pack_dirs)
|
||||
dep_errors = check_pack_dependencies(results)
|
||||
cycles = detect_dependency_cycles(results)
|
||||
|
||||
print("== Didactopus ==")
|
||||
print("Many arms, one goal — mastery.")
|
||||
print()
|
||||
|
||||
if dependency_errors:
|
||||
print("== Dependency Errors ==")
|
||||
for err in dependency_errors:
|
||||
if dep_errors:
|
||||
print("Dependency errors:")
|
||||
for err in dep_errors:
|
||||
print(f"- {err}")
|
||||
print()
|
||||
|
||||
if cycles:
|
||||
print("== Dependency Cycles ==")
|
||||
print("Dependency cycles:")
|
||||
for cycle in cycles:
|
||||
print(f"- cycle: {' -> '.join(cycle)}")
|
||||
print(f"- {' -> '.join(cycle)}")
|
||||
return
|
||||
|
||||
print("== Pack Load Order ==")
|
||||
for name in topological_pack_order(packs):
|
||||
print(f"- {name}")
|
||||
graph = build_concept_graph(results, config.platform.default_dimension_thresholds)
|
||||
mastered = set(args.mastered)
|
||||
|
||||
print("== Didactopus Concept Graph Engine ==")
|
||||
print(f"concepts: {len(graph.graph.nodes)}")
|
||||
print(f"edges: {len(graph.graph.edges)}")
|
||||
print()
|
||||
|
||||
merged = build_merged_learning_graph(packs)
|
||||
profile = LearnerProfile(
|
||||
learner_id="demo-learner",
|
||||
display_name="Demo Learner",
|
||||
goals=[args.goal],
|
||||
mastered_concepts=set(),
|
||||
hide_mastered=True,
|
||||
)
|
||||
|
||||
evidence_items = [
|
||||
EvidenceItem(
|
||||
concept_key="foundations-statistics::descriptive-statistics",
|
||||
evidence_type="project",
|
||||
score=0.88,
|
||||
is_recent=True,
|
||||
rubric_dimensions={
|
||||
"correctness": 0.9,
|
||||
"explanation": 0.83,
|
||||
"transfer": 0.79,
|
||||
"project_execution": 0.88,
|
||||
"critique": 0.74,
|
||||
},
|
||||
notes="Strong integrated performance.",
|
||||
),
|
||||
EvidenceItem(
|
||||
concept_key="bayes-extension::prior",
|
||||
evidence_type="problem",
|
||||
score=0.68,
|
||||
is_recent=True,
|
||||
rubric_dimensions={
|
||||
"correctness": 0.75,
|
||||
"explanation": 0.62,
|
||||
"transfer": 0.55,
|
||||
"critique": 0.58,
|
||||
},
|
||||
notes="Knows some basics, weak transfer and critique.",
|
||||
),
|
||||
]
|
||||
|
||||
evidence_state = ingest_evidence_bundle(
|
||||
profile=profile,
|
||||
items=evidence_items,
|
||||
resurfacing_threshold=config.platform.resurfacing_threshold,
|
||||
confidence_threshold=config.platform.confidence_threshold,
|
||||
type_weights=config.platform.evidence_weights,
|
||||
recent_multiplier=config.platform.recent_evidence_multiplier,
|
||||
dimension_thresholds=config.platform.dimension_thresholds,
|
||||
)
|
||||
|
||||
plan = build_adaptive_plan(merged, profile)
|
||||
|
||||
print("== Multi-Dimensional Evidence Summary ==")
|
||||
for concept_key, summary in evidence_state.summary_by_concept.items():
|
||||
print(
|
||||
f"- {concept_key}: weighted_mean={summary.weighted_mean_score:.2f}, "
|
||||
f"confidence={summary.confidence:.2f}, mastered={summary.mastered}"
|
||||
)
|
||||
if summary.dimension_means:
|
||||
dims = ", ".join(f"{k}={v:.2f}" for k, v in sorted(summary.dimension_means.items()))
|
||||
print(f" * dimensions: {dims}")
|
||||
if summary.weak_dimensions:
|
||||
print(f" * weak dimensions: {', '.join(summary.weak_dimensions)}")
|
||||
print(f"Target concept: {args.target}")
|
||||
print("Prerequisite chain:")
|
||||
for item in sorted(graph.prerequisite_chain(args.target)):
|
||||
print(f"- {item}")
|
||||
print()
|
||||
|
||||
print("== Mastered Concepts ==")
|
||||
if profile.mastered_concepts:
|
||||
for concept_key in sorted(profile.mastered_concepts):
|
||||
print(f"- {concept_key}")
|
||||
else:
|
||||
print("- none yet")
|
||||
print("Curriculum path from current mastery:")
|
||||
for item in graph.curriculum_path_to_target(mastered, args.target):
|
||||
print(f"- {item}")
|
||||
print()
|
||||
|
||||
print("== Next Best Concepts ==")
|
||||
for concept in plan.next_best_concepts:
|
||||
print(f"- {concept}")
|
||||
print("Ready concepts:")
|
||||
for item in graph.ready_concepts(mastered):
|
||||
print(f"- {item}")
|
||||
print()
|
||||
print("Declared related concepts for target:")
|
||||
for item in graph.related_concepts(args.target):
|
||||
print(f"- {item}")
|
||||
print()
|
||||
print("Suggested semantic links:")
|
||||
for a, b, score in suggest_semantic_links(graph, minimum_similarity=0.10)[:8]:
|
||||
print(f"- {a} <-> {b} : {score:.2f}")
|
||||
|
||||
focus_concept = "bayes-extension::prior"
|
||||
weak_dims = evidence_state.summary_by_concept.get(focus_concept).weak_dimensions if focus_concept in evidence_state.summary_by_concept else []
|
||||
print(generate_socratic_prompt(provider, focus_concept, weak_dims))
|
||||
print(generate_practice_task(provider, focus_concept, weak_dims))
|
||||
print(suggest_capstone(provider, args.domain))
|
||||
if args.export_dot:
|
||||
graph.export_graphviz(args.export_dot)
|
||||
print(f"Exported Graphviz DOT to {args.export_dot}")
|
||||
if args.export_cytoscape:
|
||||
graph.export_cytoscape_json(args.export_cytoscape)
|
||||
print(f"Exported Cytoscape JSON to {args.export_cytoscape}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
|
|||
|
|
@ -13,10 +13,6 @@ class ModelProvider:
|
|||
def __init__(self, config: ModelProviderConfig) -> None:
|
||||
self.config = config
|
||||
|
||||
def describe(self) -> str:
|
||||
local = self.config.local
|
||||
return f"mode={self.config.mode}, local={local.backend}:{local.model_name}"
|
||||
|
||||
def generate(self, prompt: str) -> ModelResponse:
|
||||
local = self.config.local
|
||||
preview = prompt.strip().replace("\n", " ")[:120]
|
||||
|
|
|
|||
|
|
@ -1,34 +1,39 @@
|
|||
from dataclasses import dataclass
|
||||
from typing import Dict, List
|
||||
from typing import Any
|
||||
|
||||
|
||||
@dataclass
|
||||
class ProfileTemplate:
|
||||
name: str
|
||||
required_dimensions: List[str]
|
||||
dimension_threshold_overrides: Dict[str, float]
|
||||
|
||||
|
||||
def resolve_mastery_profile(concept_profile, templates, default_profile):
|
||||
if concept_profile is None:
|
||||
return default_profile
|
||||
|
||||
template_name = concept_profile.get("template")
|
||||
if template_name:
|
||||
base = templates.get(template_name, default_profile)
|
||||
profile = {
|
||||
"required_dimensions": list(base.required_dimensions),
|
||||
"dimension_threshold_overrides": dict(base.dimension_threshold_overrides),
|
||||
}
|
||||
def resolve_mastery_profile(
|
||||
concept_profile: dict[str, Any] | None,
|
||||
templates: dict[str, dict[str, Any]],
|
||||
default_thresholds: dict[str, float],
|
||||
) -> dict[str, Any]:
|
||||
default_profile = {
|
||||
"required_dimensions": list(default_thresholds.keys()),
|
||||
"dimension_threshold_overrides": {},
|
||||
}
|
||||
if not concept_profile:
|
||||
effective = dict(default_profile)
|
||||
else:
|
||||
profile = default_profile.copy()
|
||||
template_name = concept_profile.get("template")
|
||||
if template_name and template_name in templates:
|
||||
tmpl = templates[template_name]
|
||||
effective = {
|
||||
"required_dimensions": list(tmpl.get("required_dimensions", default_profile["required_dimensions"])),
|
||||
"dimension_threshold_overrides": dict(tmpl.get("dimension_threshold_overrides", {})),
|
||||
}
|
||||
else:
|
||||
effective = dict(default_profile)
|
||||
|
||||
if "required_dimensions" in concept_profile:
|
||||
profile["required_dimensions"] = concept_profile["required_dimensions"]
|
||||
if concept_profile.get("required_dimensions"):
|
||||
effective["required_dimensions"] = list(concept_profile["required_dimensions"])
|
||||
if concept_profile.get("dimension_threshold_overrides"):
|
||||
effective["dimension_threshold_overrides"].update(
|
||||
concept_profile["dimension_threshold_overrides"]
|
||||
)
|
||||
|
||||
if "dimension_threshold_overrides" in concept_profile:
|
||||
profile["dimension_threshold_overrides"].update(
|
||||
concept_profile["dimension_threshold_overrides"]
|
||||
)
|
||||
|
||||
return profile
|
||||
thresholds = dict(default_thresholds)
|
||||
thresholds.update(effective["dimension_threshold_overrides"])
|
||||
return {
|
||||
"required_dimensions": effective["required_dimensions"],
|
||||
"dimension_threshold_overrides": dict(effective["dimension_threshold_overrides"]),
|
||||
"effective_thresholds": {dim: thresholds[dim] for dim in effective["required_dimensions"] if dim in thresholds},
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,37 @@
|
|||
from collections import Counter
|
||||
import math
|
||||
|
||||
|
||||
def _tokenize(text: str) -> list[str]:
|
||||
cleaned = "".join(ch.lower() if ch.isalnum() else " " for ch in text)
|
||||
return [tok for tok in cleaned.split() if tok]
|
||||
|
||||
|
||||
def token_cosine_similarity(text_a: str, text_b: str) -> float:
|
||||
tokens_a = _tokenize(text_a)
|
||||
tokens_b = _tokenize(text_b)
|
||||
if not tokens_a or not tokens_b:
|
||||
return 0.0
|
||||
ca = Counter(tokens_a)
|
||||
cb = Counter(tokens_b)
|
||||
shared = set(ca) & set(cb)
|
||||
dot = sum(ca[t] * cb[t] for t in shared)
|
||||
na = math.sqrt(sum(v * v for v in ca.values()))
|
||||
nb = math.sqrt(sum(v * v for v in cb.values()))
|
||||
if na == 0 or nb == 0:
|
||||
return 0.0
|
||||
return dot / (na * nb)
|
||||
|
||||
|
||||
def concept_similarity(concept_a: dict, concept_b: dict) -> float:
|
||||
text_a = " ".join([
|
||||
concept_a.get("title", ""),
|
||||
concept_a.get("description", ""),
|
||||
" ".join(concept_a.get("mastery_signals", [])),
|
||||
])
|
||||
text_b = " ".join([
|
||||
concept_b.get("title", ""),
|
||||
concept_b.get("description", ""),
|
||||
" ".join(concept_b.get("mastery_signals", [])),
|
||||
])
|
||||
return token_cosine_similarity(text_a, text_b)
|
||||
|
|
@ -0,0 +1,44 @@
|
|||
from didactopus.artifact_registry import discover_domain_packs
|
||||
from didactopus.config import load_config
|
||||
from didactopus.graph_builder import build_concept_graph, suggest_semantic_links
|
||||
|
||||
|
||||
def test_concept_graph_builds() -> None:
|
||||
config = load_config("configs/config.example.yaml")
|
||||
results = discover_domain_packs(["domain-packs"])
|
||||
graph = build_concept_graph(results, config.platform.default_dimension_thresholds)
|
||||
assert "foundations-statistics::probability-basics" in graph.graph.nodes
|
||||
assert "bayes-extension::posterior" in graph.graph.nodes
|
||||
|
||||
|
||||
def test_prerequisite_path() -> None:
|
||||
config = load_config("configs/config.example.yaml")
|
||||
results = discover_domain_packs(["domain-packs"])
|
||||
graph = build_concept_graph(results, config.platform.default_dimension_thresholds)
|
||||
path = graph.learning_path("bayes-extension::prior", "bayes-extension::posterior")
|
||||
assert path == ["bayes-extension::prior", "bayes-extension::posterior"]
|
||||
|
||||
|
||||
def test_curriculum_path_to_target() -> None:
|
||||
config = load_config("configs/config.example.yaml")
|
||||
results = discover_domain_packs(["domain-packs"])
|
||||
graph = build_concept_graph(results, config.platform.default_dimension_thresholds)
|
||||
path = graph.curriculum_path_to_target(set(), "bayes-extension::posterior")
|
||||
assert "bayes-extension::prior" in path
|
||||
assert "bayes-extension::posterior" in path
|
||||
|
||||
|
||||
def test_declared_cross_pack_links_exist() -> None:
|
||||
config = load_config("configs/config.example.yaml")
|
||||
results = discover_domain_packs(["domain-packs"])
|
||||
graph = build_concept_graph(results, config.platform.default_dimension_thresholds)
|
||||
related = graph.related_concepts("bayes-extension::posterior")
|
||||
assert "applied-inference::model-checking" in related
|
||||
|
||||
|
||||
def test_semantic_link_suggestions() -> None:
|
||||
config = load_config("configs/config.example.yaml")
|
||||
results = discover_domain_packs(["domain-packs"])
|
||||
graph = build_concept_graph(results, config.platform.default_dimension_thresholds)
|
||||
suggestions = suggest_semantic_links(graph, minimum_similarity=0.10)
|
||||
assert len(suggestions) >= 1
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
from pathlib import Path
|
||||
from didactopus.artifact_registry import discover_domain_packs
|
||||
from didactopus.config import load_config
|
||||
from didactopus.graph_builder import build_concept_graph
|
||||
|
||||
|
||||
def test_exports(tmp_path: Path) -> None:
|
||||
config = load_config("configs/config.example.yaml")
|
||||
results = discover_domain_packs(["domain-packs"])
|
||||
graph = build_concept_graph(results, config.platform.default_dimension_thresholds)
|
||||
|
||||
dot_path = tmp_path / "graph.dot"
|
||||
json_path = tmp_path / "graph.json"
|
||||
|
||||
graph.export_graphviz(str(dot_path))
|
||||
graph.export_cytoscape_json(str(json_path))
|
||||
|
||||
assert dot_path.exists()
|
||||
assert json_path.exists()
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
from didactopus.profile_templates import resolve_mastery_profile
|
||||
|
||||
|
||||
def test_template_resolution() -> None:
|
||||
templates = {
|
||||
"foundation": {
|
||||
"required_dimensions": ["correctness", "explanation"],
|
||||
"dimension_threshold_overrides": {"explanation": 0.8},
|
||||
}
|
||||
}
|
||||
resolved = resolve_mastery_profile(
|
||||
{"template": "foundation"},
|
||||
templates,
|
||||
{"correctness": 0.8, "explanation": 0.75, "transfer": 0.7},
|
||||
)
|
||||
assert resolved["required_dimensions"] == ["correctness", "explanation"]
|
||||
assert resolved["effective_thresholds"]["correctness"] == 0.8
|
||||
assert resolved["effective_thresholds"]["explanation"] == 0.8
|
||||
Loading…
Reference in New Issue