Making invisible progress feel tangible
Led product design for a sensory substitution platform translating sound into touch.
Context
Neosensory translates sound into haptic signals.
Instead of amplifying hearing, users must learn a new sensory language.
Early adoption revealed a critical issue:
Users dropped off within the first weeks.
The system worked.
The learning did not.
The problem
Early experiences felt indistinguishable.
Users described it as: “everything feels the same.”
This ambiguity was interpreted as failure, not learning.
Engineering focused on signal quality.
I reframed the problem:
The issue was not accuracy.
It was expectation.
Users didn’t know what progress should feel like.
The shift
I shifted the focus from signal performance to learning design.
From:
- accuracy → perception
- output → progression
- correctness → confidence
Retention depended on perceived competence, not actual accuracy.
The system
The product was restructured as a staged learning system:
Each day opens with one short session and one lesson — not a dashboard of options. Repetition before variety.
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1
Onboarding
Normalize ambiguity and set expectations
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2
Differentiation
Help users detect meaningful differences
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3
Immersion
Transition learning into real-world use
The goal was not faster accuracy.
It was sustained engagement through uncertainty.
Immersion meant leaving the app. Passive practice in ordinary moments — making coffee, walking — is where transfer happens.
Design decisions
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Perceived competence over accuracy
Users disengage when they feel they’re failing.
We prioritized confidence signals alongside performance.
Result: increased retention and continued engagement.
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Staged exposure over signal complexity
Reduced early signal variety and increased repetition.
Result: faster differentiation and lower cognitive load.
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Expectation framing over silent onboarding
Made ambiguity explicit and expected.
Result: confusion interpreted as progress, not failure.
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Timing over constant feedback
Reduced feedback frequency and improved timing.
Result: deeper internal calibration and learning.
Behavioral signals
We measured learning through behavior, not just performance:
The Day 3 check-in reframed ambiguity as signal. Whether users felt a shift or not, both answers kept them moving.
- Perceived competence — Strong predictor of retention between Weeks 2–4
- Time to first success — Day 3 milestone correlated with continuation
- Emotional interpretation — Framing confusion as learning reduced drop-off
- Return behavior — Increased session consistency after early progress
Tradeoffs
Designing for learning required restraint:
- Slower initial progress vs faster perceived progress
- Less feedback vs clearer signals
- Simpler early experience vs full system exposure
We prioritized progression over completeness.
Impact
Weekly reflections celebrated return behavior, not test scores.
- Reduced early drop-off
- Increased 3-month retention
- Higher 12-week completion
- Increased NPS and confidence
Retention moved from fragile to structured.
Key insight
Users don’t drop off because systems fail. They drop off because they believe they are failing.
Reflection
This project changed how I think about learning systems.
Users don’t disengage because they can’t improve.
They disengage because they don’t recognize progress.
Key takeaways:
- perceived competence drives retention more than accuracy
- early micro-success determines long-term engagement
- ambiguity must be framed, not removed
Designing for learning means designing for belief, not just performance.