Hello, World!

Abbett Labs — personalized vocabulary learning at mobile scale

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
Hello, World! - Abbett Labs

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.

Key metrics

Personal memories used as contextual vocabulary anchors
Photos
Machine learning for individualized lesson generation at scale
ML
Year co-founded and shipped to the Apple App Store
2020
Core principles: context, augmentation, simplicity, and scalable personalization
4

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

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

View system: Human Learning System

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

Stronger vocabulary recallHigher engagement vs. generic listsLearning anchored to real life

Personalized vocabulary learning — shipped to the App Store

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