- Client
- Abbett Labs
- Role
- Co-founder & Product Director
- Timeline
- 2018–2021
- Industry
- Education / EdTech
- System
- Human Learning
- Domain
- Education
- Design Lever
- Personalisation
- Primary Outcome
- Retention
- Framework
- Human Learning System
Executive summary
Language learning platforms still treat every learner the same — generic lists, standardized drills, no connection to personal experience. As co-founder and product director, the work reframed the category question: how to deliver one-to-one relevance at digital scale.
Result: Photo-anchored vocabulary experiences. ML-generated personalized lessons. A consumer app shipped on the Apple App Store — validating that personalization can be the core product, not a feature bolt-on.
Key outcomes
- Defined a product vision connecting cognitive science, AI, and mobile-first learning behavior
- Shipped vocabulary experiences anchored to personal photographs instead of generic word lists
- Used machine learning to generate individualized content without manual lesson creation per learner
- Launched on the Apple App Store — moving personalized learning from research hypothesis to usable product
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.
Hello, World! Learning Operating System
How Hello, World! turns personal photos into scalable, individualized vocabulary learning
HELLO, WORLD! — LEARNING OPERATING SYSTEM
Research foundations
Context-dependent memory
Spaced retrieval
ML lesson generation
Personal Learning Engine
Learner context
- Personal photographs
- Life memories & travel
- Daily experiences
- Individual vocabulary gaps
Hello, World! stack
- ML-generated lessons
- Photo-anchored content
- iOS consumer app
- Personalization at scale
In-app learning loop
Photo context
Vocabulary capture
Spaced retrieval
Reflection
Learner outcomes
Learning anchored to real lifePersonalized vocabulary learning — shipped to the App Store
Hello, World!
Abbett Labs — personalized vocabulary learning at mobile scale
Key metrics
Photos
Personal memories used as contextual vocabulary anchors
ML
Machine learning for individualized lesson generation at scale
2020
Year co-founded and shipped to the Apple App Store
4
Core principles: context, augmentation, simplicity, and scalable personalization
Framework
Human Learning System
The Human Learning System maps learner profile, vocabulary goals, usage patterns, and spaced-repetition intervals through an adaptive learning engine to retained vocabulary, learning habits, and measurable individual progress. Inputs are behavioural and contextual; the core system personalises content delivery based on retention signals; outputs are durable learning outcomes, not session metrics. This framework emerged from Hello, World! as a reusable model for any engagement where individual context determines whether information sticks.
Inputs
Core System
Adaptive learning engine that personalises content delivery based on retention signals
Outputs