Human Learning System
Adaptive learning framework that personalises vocabulary delivery through learner context, retention signals, and spaced practice.
Inputs
Learner profileVocabulary goalsUsage patternsSpaced repetition intervals
Core System
Adaptive learning engine that personalises content delivery based on retention signals
Outputs
Retained vocabularyLearning habitsMeasurable progressSustained engagement
Core principles
- Personalization without retention measurement is experimentation, not strategy — design the feedback loop before the feature roadmap.
- Learning systems should anchor content in lived experience; generic curricula optimize for content production, not memory formation.
- AI belongs in the learning engine that adapts delivery — not as language on the landing page that promises intelligence the product cannot demonstrate.
- Consumer education products must reconcile cognitive science with distribution economics; the architecture is where that reconciliation happens.