71 lines
2.1 KiB
Markdown
71 lines
2.1 KiB
Markdown
# Architecture Overview
|
|
|
|
## System aim
|
|
|
|
Didactopus supports mastery-oriented autodidactic learning across many domains while reducing the risk that AI becomes a crutch for superficial performance.
|
|
|
|
## Top-level architecture
|
|
|
|
```text
|
|
Learner Interface
|
|
|
|
|
v
|
|
Orchestration Layer
|
|
|- learner profile
|
|
|- session state
|
|
|- competency tracker
|
|
|- artifact registry
|
|
|
|
|
+--> Domain Mapping Engine
|
|
+--> Curriculum Generator
|
|
+--> Mentor Agent
|
|
+--> Practice Generator
|
|
+--> Project Advisor
|
|
+--> Evaluation System
|
|
|
|
|
v
|
|
Model Provider Abstraction
|
|
|- local model backends
|
|
|- optional remote backends
|
|
```
|
|
|
|
## Core data objects
|
|
|
|
- **LearnerProfile**: goals, prior knowledge, pacing, artifacts, assessment history
|
|
- **ConceptNode**: concept, prerequisites, representative tasks, mastery criteria
|
|
- **RoadmapStage**: stage goals, concepts, practice forms, project milestones
|
|
- **EvidenceItem**: explanations, solved problems, project artifacts, benchmark scores
|
|
- **EvaluationReport**: rubric scores, weaknesses, suggested remediation
|
|
- **ArtifactManifest**: metadata for a domain pack or other contributed artifact
|
|
|
|
## Critical design constraint
|
|
|
|
The platform should optimize for **competence evidence** rather than conversational fluency. A learner should not advance based solely on sounding knowledgeable.
|
|
|
|
## Local-first inference
|
|
|
|
The provider abstraction should support:
|
|
- Ollama
|
|
- llama.cpp HTTP servers
|
|
- LM Studio local server
|
|
- vLLM or comparable self-hosted inference
|
|
- optional remote APIs only by explicit configuration
|
|
|
|
## Artifact ecosystem
|
|
|
|
The architecture should support:
|
|
- first-party curated packs
|
|
- third-party domain packs
|
|
- version validation
|
|
- compatibility checks
|
|
- offline local discovery
|
|
|
|
## Safety against shallow learning
|
|
|
|
The orchestration layer should support policies such as:
|
|
- forcing first-attempt learner answers
|
|
- hiding worked solutions until after effort is shown
|
|
- requiring self-explanation
|
|
- issuing counterexamples and adversarial probes
|
|
- cross-checking claims against references and experiments where applicable
|