Didactopus/docs/agentic-learner-loop.md

Agentic Learner Loop

The agentic learner loop is the first closed-loop prototype for AI-student behavior in Didactopus.

Current loop

1. Inspect current mastery state
2. Ask graph-aware planner for next best concept
3. Produce synthetic attempt
4. Score attempt into evidence
5. Update mastery state
6. Repeat until target is reached or iteration budget ends

Important limitation

The current implementation is a scaffold. The learner attempt is synthetic and deterministic, not a true external model call with robust domain evaluation.

Why it still matters

It establishes the orchestration pattern for:

• planner-guided concept selection
• evidence accumulation
• mastery updates
• goal-directed progression