# FAQ

## How is an AI student's learned mastery represented?

As structured operational state, including:
- mastered concepts
- evaluator summaries
- weak dimensions
- evidence records
- artifacts
- capability export

## Does Didactopus change the AI model weights?

No. In the current architecture, Didactopus supervises and evaluates a learner
agent, but it does not retrain the foundation model.

## How is an AI student ready to be put to work?

Readiness is represented operationally. A downstream system can inspect:
- which concepts are mastered
- which weak dimensions remain
- what artifacts were produced
- what evaluator evidence supports deployment

## Is the capability export a certification?

Not by itself. It is a structured mastery report. In future, it could be combined
with formal evaluators, signed evidence records, and policy rules.

## Why is this useful?

Because it allows Didactopus outputs to feed into:
- task routing
- portfolio review
- benchmark comparison
- agent deployment policies