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