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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:
- Learner state + pack graph → animation frames
- Frames exported as SVG
- Render bundle created
- 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