Didactopus/docs/evidence-engine.md

Evidence Engine

Purpose

The evidence engine updates learner mastery state from observed work rather than from manual declarations alone.

Evidence types in this revision

• explanation
• problem
• project
• transfer

Each evidence item includes:

• target concept
• evidence type
• score from 0.0 to 1.0
• optional notes

Current update policy

For each concept:

• maintain a running average evidence score
• mark as mastered when average score meets mastery threshold and at least one evidence item exists
• resurface a mastered concept when the average later drops below the resurfacing threshold

This is intentionally simple and transparent.

Future work

• weighted evidence types
• recency decay
• uncertainty estimates
• Bayesian mastery models
• multi-rubric scoring per concept