219 lines
7.0 KiB
Markdown
219 lines
7.0 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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## Recent revisions
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### Multi-Source Course Ingestion
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This revision adds a **Multi-Source Course Ingestion Layer**.
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The pipeline can now accept multiple source files representing the same course or
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topic domain, normalize them into a shared intermediate representation, merge them,
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and emit a single draft Didactopus pack plus a conflict report.
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#### Supported scaffold source types
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Current scaffold adapters:
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- Markdown (`.md`)
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- Plain text (`.txt`)
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- HTML-ish text (`.html`, `.htm`)
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- Transcript text (`.transcript.txt`)
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- Syllabus text (`.syllabus.txt`)
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This revision is intentionally adapter-oriented, so future PDF, slide, and DOCX
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adapters can be added behind the same interface.
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#### What is included
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- multi-source adapter dispatch
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- normalized source records
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- source merge logic
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- cross-source terminology conflict report
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- duplicate lesson/title detection
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- merged draft pack emission
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- merged attribution manifest
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- sample multi-source inputs
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- sample merged output pack
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### Course Ingestion Pipeline
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This revision adds a **Course-to-Pack Ingestion Pipeline** plus a **stable rule-policy adapter layer**.
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The design goal is to turn open or user-supplied course materials into draft
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Didactopus domain packs without introducing a brittle external rule-engine dependency.
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#### Why no third-party rule engine here?
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To minimize dependency risk, this scaffold uses a small declarative rule-policy
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adapter implemented in pure Python and standard-library data structures.
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That gives Didactopus:
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- portable rules
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- inspectable rule definitions
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- deterministic behavior
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- zero extra runtime dependency for policy evaluation
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If a stronger rule engine is needed later, this adapter can remain the stable API surface.
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#### What is included
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- normalized course schema
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- Markdown/HTML-ish text ingestion adapter
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- module / lesson / objective extraction
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- concept candidate extraction
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- prerequisite guess generation
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- rule-policy adapter
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- draft pack emitter
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- review report generation
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- sample course input
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- sample generated pack outputs
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### Mastery Ledger
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This revision adds a **Mastery Ledger + Capability Export** layer.
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The main purpose is to let Didactopus turn accumulated learner state into
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portable, inspectable artifacts that can support downstream deployment,
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review, orchestration, or certification-like workflows.
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#### What is new
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- mastery ledger data model
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- capability profile export
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- JSON export of mastered concepts and evaluator summaries
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- Markdown export of a readable capability report
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- artifact manifest for produced deliverables
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- demo CLI for generating exports for an AI student or human learner
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- FAQ covering how learned mastery is represented and put to work
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#### Why this matters
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Didactopus can now do more than guide learning. It can also emit a structured
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statement of what a learner appears able to do, based on explicit concepts,
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evidence, and artifacts.
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That makes it easier to use Didactopus as:
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- a mastery tracker
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- a portfolio generator
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- a deployment-readiness aid
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- an orchestration input for agent routing
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#### Mastery representation
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A learner's mastery is represented as structured operational state, including:
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- mastered concepts
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- evaluator results
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- evidence summaries
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- weak dimensions
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- attempt history
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- produced artifacts
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- capability export
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This is stricter than a normal chat transcript or self-description.
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#### Future direction
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A later revision should connect the capability export with:
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- formal evaluator outputs
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- signed evidence ledgers
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- domain-specific capability schemas
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- deployment policies for agent routing
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### Evaluator Pipeline
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This revision introduces a **pluggable evaluator pipeline** that converts
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learner attempts into structured mastery evidence.
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### Agentic Learner Loop
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This revision adds an **agentic learner loop** that turns Didactopus into a closed-loop mastery system prototype.
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The loop can now:
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- choose the next concept via the graph-aware planner
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- generate a synthetic learner attempt
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- score the attempt into evidence
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- update mastery state
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- repeat toward a target concept
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This is still scaffold-level, but it is the first explicit implementation of the idea that **Didactopus can supervise not only human learners, but also AI student agents**.
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## Complete overview to this point
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Didactopus currently 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** for cross-pack prerequisite reasoning, linking, pathfinding, and export
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- **Adaptive learner engine** for 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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- **Graph-aware planner** for utility-ranked next-step recommendations
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- **Agentic learner loop** for iterative goal-directed mastery acquisition
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## Agentic AI students
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An AI student under Didactopus is modeled as an **agent that accumulates evidence against concept mastery criteria**.
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It does not “learn” in the same sense that model weights are retrained inside Didactopus. Instead, its learned mastery is represented as:
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- current mastered concept set
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- evidence history
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- dimension-level competence summaries
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- concept-specific weak dimensions
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- adaptive plan state
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- optional artifacts, explanations, project outputs, and critiques it has produced
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In other words, Didactopus represents mastery as a **structured operational state**, not merely a chat transcript.
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That state can be put to work by:
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- selecting tasks the agent is now qualified to attempt
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- routing domain-relevant problems to the agent
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- exposing mastered concept profiles to orchestration logic
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- using evidence summaries to decide whether the agent should act, defer, or review
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- exporting a mastery portfolio for downstream use
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## FAQ
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See:
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- `docs/faq.md`
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## Correctness and formal knowledge components
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See:
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- `docs/correctness-and-knowledge-engine.md`
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Short version: yes, there is a strong argument that Didactopus will eventually benefit from a more formal knowledge-engine layer, especially for domains where correctness can be stated in symbolic, logical, computational, or rule-governed terms.
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A good future architecture is likely **hybrid**:
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- LLM/agentic layer for explanation, synthesis, critique, and exploration
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- formal knowledge engine for rule checking, constraint satisfaction, proof support, symbolic validation, and executable correctness checks
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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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