1.8 KiB
Didactopus
Didactopus is a local-first AI-assisted autodidactic mastery platform built around concept graphs, evaluator-driven evidence, adaptive planning, mastery ledgers, curriculum ingestion, and human review of generated draft packs.
This revision
This revision adds a domain-pack semantic QA layer.
The goal is to go beyond file integrity and cross-file coherence, and start asking whether a generated Didactopus pack looks semantically plausible as a learning domain.
Why this matters
A pack may pass structural validation and still have higher-level weaknesses such as:
- near-duplicate concepts with different wording
- prerequisites that look suspiciously thin or over-compressed
- missing bridge concepts between stages
- concepts that are probably too broad and should be split
- concepts with names that imply overlap or ambiguity
Those problems can still slow a learner or curator down, which means they still contribute to the activation-energy hump Didactopus is meant to reduce.
What is included
- semantic QA analysis module
- heuristic semantic checks
- semantic QA findings included in import preview
- UI panel for semantic QA warnings
- sample packs showing semantic QA output
- tests for semantic QA behavior
Current semantic QA checks
This scaffold includes heuristic checks for:
- near-duplicate concept titles
- over-broad concept titles
- suspiciously thin prerequisite chains
- missing bridge concepts between roadmap stages
- concepts with very similar descriptions
- singleton advanced stages with no visible bridge support
Design stance
This is still a heuristic layer, not a final semantic truth engine.
Its purpose is to surface likely curation issues early enough that a reviewer can correct them before those issues turn into confusion or wasted effort.