# Didactopus FAQ

## What is Didactopus for?

Didactopus helps represent learning as a knowledge graph with evidence, mastery,
artifacts, and reusable outputs. It supports both learners and the systems that
author, review, and improve learning materials.

## Is it only for AI learners?

No. It is built for:

- human learners
- AI learners
- hybrid workflows where AI and humans both contribute

## Why emphasize synthesis?

Because understanding often improves when learners recognize structural overlap
between different domains. Transfer, analogy, and conceptual reuse are central to
real intellectual progress.

Examples include:

- entropy in thermodynamics and information theory
- drift in population genetics and random walks
- feedback in engineering, biology, and machine learning

Didactopus tries to surface these overlaps rather than treating subjects as sealed
containers.

## Why not automatically trust learner-derived knowledge?

Learner-derived knowledge can be valuable, but it still needs review,
validation, and provenance. A learner may discover something surprising and
useful, but the system should preserve both usefulness and caution.

## What can learner-derived knowledge become?

Depending on review outcome, it can be promoted into:

- accepted pack improvements
- curriculum drafts
- reusable skill bundles
- archived but unadopted suggestions

## What is the review-and-promotion workflow?

It is the process by which exported learner observations are triaged, reviewed,
validated, and either promoted or archived.

## What is the synthesis engine?

The synthesis engine analyzes concept graphs and learner evidence to identify
candidate conceptual overlaps, analogies, and transferable structures across
packs.

## Can Didactopus produce traditional educational outputs?

Yes. Knowledge exports can seed:

- lesson outlines
- study guides
- exercise sets
- instructor notes
- curriculum maps

## Can Didactopus produce AI skill-like outputs?

Yes. Structured exports can support:

- skill manifests
- evaluation checklists
- failure-mode notes
- canonical examples
- prerequisite maps

## What happens to artifacts over time?

Artifacts can be:

- retained
- archived
- expired
- soft-deleted

Retention policy support is included so temporary debugging products and durable
portfolio artifacts can be treated differently.