100 lines
3.7 KiB
Markdown
100 lines
3.7 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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This revision introduces a **pluggable evaluator pipeline** that converts
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learner attempts into structured mastery evidence.
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The prior 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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# Didactopus
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Didactopus is an AI-assisted autodidactic mastery platform based on
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concept graphs, mastery evidence, and evaluator-driven correctness.
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|
|
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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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