1019 B
1019 B
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