182 lines
4.1 KiB
Markdown
182 lines
4.1 KiB
Markdown
|
||
# Didactopus
|
||
|
||
Didactopus is an experimental learning infrastructure designed to support **human learners, AI learners, and hybrid learning ecosystems**. It focuses on representing knowledge structures, learner progress, and the evolution of understanding in ways that are inspectable, reproducible, and reusable.
|
||
|
||
The system treats learning as an **observable graph process** rather than a sequence of isolated exercises. Concept nodes, prerequisite edges, and learner evidence events together produce a dynamic knowledge trajectory.
|
||
|
||
Didactopus aims to support:
|
||
|
||
- individual mastery learning
|
||
- curriculum authoring
|
||
- discovery of new conceptual connections
|
||
- AI‑assisted autodidactic learning
|
||
- generation of reusable educational artifacts
|
||
|
||
---
|
||
|
||
# Core Concepts
|
||
|
||
## Domain Packs
|
||
|
||
A **domain pack** represents a structured set of concepts and relationships.
|
||
Concepts form nodes in a graph and may include:
|
||
|
||
- prerequisites
|
||
- cross‑pack links
|
||
- exercises or learning activities
|
||
- conceptual metadata
|
||
|
||
Domain packs can be:
|
||
|
||
- private (learner owned)
|
||
- community shared
|
||
- curated / mentor‑reviewed
|
||
|
||
---
|
||
|
||
## Learner State
|
||
|
||
Each learner accumulates **evidence events** that update mastery estimates for concepts.
|
||
|
||
Evidence events can include:
|
||
|
||
- exercises
|
||
- reviews
|
||
- projects
|
||
- observations
|
||
- mentor evaluation
|
||
|
||
Mastery records track:
|
||
|
||
- score
|
||
- confidence
|
||
- evidence count
|
||
- update history
|
||
|
||
The system stores full evidence history so that learning trajectories can be reconstructed.
|
||
|
||
---
|
||
|
||
## Artifact System
|
||
|
||
Didactopus produces **artifacts** that document learner knowledge and learning trajectories.
|
||
|
||
Artifacts may include:
|
||
|
||
- animation bundles
|
||
- graph visualizations
|
||
- knowledge exports
|
||
- curriculum drafts
|
||
- derived skill descriptions
|
||
|
||
Artifacts are tracked using an **artifact registry** with lifecycle metadata.
|
||
|
||
Artifact lifecycle states include:
|
||
|
||
- created
|
||
- retained
|
||
- expired
|
||
- deleted
|
||
|
||
Retention policies allow systems to manage storage while preserving important learner discoveries.
|
||
|
||
---
|
||
|
||
# Worker Rendering System
|
||
|
||
Rendering jobs transform learner knowledge into visual or structured outputs.
|
||
|
||
Typical workflow:
|
||
|
||
1. Learner state + pack graph → animation frames
|
||
2. Frames exported as SVG
|
||
3. Render bundle created
|
||
4. Optional FFmpeg render to GIF/MP4
|
||
|
||
Outputs are registered as artifacts so they can be downloaded or reused.
|
||
|
||
---
|
||
|
||
# Knowledge Export
|
||
|
||
Didactopus supports exporting structured learner knowledge for reuse.
|
||
|
||
Export targets include:
|
||
|
||
- improved domain packs
|
||
- curriculum material
|
||
- AI training data
|
||
- agent skill definitions
|
||
- research on learning processes
|
||
|
||
Exports are **candidate knowledge**, not automatically validated truth.
|
||
Human mentors or automated validation pipelines can review them before promotion.
|
||
|
||
---
|
||
|
||
# Philosophy: Synthesis and Discovery
|
||
|
||
Didactopus places strong emphasis on **synthesis**.
|
||
|
||
Many important discoveries occur not within a single domain, but at the **intersection of domains**.
|
||
|
||
Examples include:
|
||
|
||
- mathematics applied to biology
|
||
- information theory applied to neuroscience
|
||
- physics concepts applied to ecological models
|
||
|
||
Domain packs therefore support:
|
||
|
||
- cross‑pack links
|
||
- relationship annotations
|
||
- visualization of conceptual overlap
|
||
|
||
These connections help learners discover:
|
||
|
||
- analogies
|
||
- transferable skills
|
||
- deeper structural patterns across knowledge fields
|
||
|
||
The goal is not merely to learn isolated facts, but to build a **network of understanding**.
|
||
|
||
---
|
||
|
||
# Learners as Discoverers
|
||
|
||
Learners sometimes discover insights that mentors did not anticipate.
|
||
|
||
Didactopus is designed so that learner output can contribute back into the system through:
|
||
|
||
- knowledge export
|
||
- artifact review workflows
|
||
- pack improvement suggestions
|
||
|
||
This creates a **feedback loop** where learning activity improves the curriculum itself.
|
||
|
||
---
|
||
|
||
# Intended Uses
|
||
|
||
Didactopus supports several categories of use:
|
||
|
||
Human learning
|
||
- self‑directed study
|
||
- classroom support
|
||
- mastery‑based curricula
|
||
|
||
Research
|
||
- studying learning trajectories
|
||
- analyzing conceptual difficulty
|
||
|
||
AI systems
|
||
- training agent skill graphs
|
||
- evaluating reasoning development
|
||
|
||
Educational publishing
|
||
- curriculum drafts
|
||
- visualization tools
|
||
- learning progress reports
|
||
|