Meta-Learning Optimization Engine
Learn-to-learn acceleration via task curriculum adaptation
Learning-to-Learn Paradigm
Optimizes task sequence to maximize transfer efficiency. Each session teaches child how to learn faster on future tasks.
Baseline Assessment
Learn current state
20 min
Skill A (easy)
Establish baseline mastery
10 min
Skill A (hard)
Identify ceiling
15 min
Skill B (transfer)
Test cross-domain learning
12 min
Meta-reflection
Update learning strategy
8 min
Meta-Cognitive Markers
- • Strategy awareness (child can articulate approach)
- • Metacognitive monitoring (self-correction rate)
- • Transfer prediction accuracy (pre/post confidence)
- • Learning curve steepness (skill acquisition rate)
Acceleration Effect
Meta-learning boosts task 3+ learning speed by ~30% (learning curve exponent -0.3 → -0.45). Child internalizes learning strategy faster.
