Project 01 — 2026
Nara — An AI learning companion that plans, explains, and adapts
A study companion that turns AI output into something learners can actually trust and follow.
- Role
- Product Designer — 0→1
- Timeline
- 4 months
- Team
- 2 engineers, 1 PM, me
- Scope
- ResearchUXUIPrototypingAI patterns
Context
An early-stage team wanted to build a learning product around LLMs, but the market was already flooded with “AI tutors” that generate content nobody finishes. They asked me to shape the product from zero: positioning, experience, and interface.
My first job wasn't screens. It was figuring out what an AI learning product should actually promise — and to whom.
The problem
Learners don't lack content; they lack a plan they believe in. Generic AI tutors respond to whatever is asked, so the learner carries the cognitive load of deciding what to study, in what order, and whether the answer is even right.
Research interviews with 12 students and self-learners kept circling the same three pains: plans collapse after day three, AI answers feel unaccountable, and progress is invisible.
Framing
We reframed the product from “an AI that answers” to “a companion that commits.” Nara would not just generate study material — it would commit to a plan, show its reasoning, and adjust openly when the learner falls behind.
That framing turned trust from a vague principle into three concrete design constraints: always show why the AI suggested something, make the plan editable by the learner, and never hide a change the AI made.
Key decisions
Decision 01
Show the reasoning, not just the answer
Every AI suggestion ships with a one-line rationale (“adding recall practice — you missed 3 of 5 flashcards this week”). Learners override it in one tap, which paradoxically increased trust in suggestions they kept.
Decision 02
A weekly plan as the home, not a chat box
Chat invites unbounded prompting; a plan gives the product an opinion. The home screen is a seven-day plan the learner can drag, swap, or shrink — the AI proposes, the learner disposes.
Decision 03
Calm density over clever empty states
Instead of decorative empty states, every screen answers “what should I do next, and why.” Progress is shown as momentum (streak of completed intentions), not as a guilt-inducing percentage.
The solution
A plan you can argue with
The weekly plan is the product's spine. Each card carries the AI's rationale, an effort estimate, and a one-tap override — so the learner stays in control without doing the planning work alone.
Explain-as-you-learn
Inside a session, explanations arrive in layers: a plain answer first, the reasoning behind it on demand, and related gaps the AI noticed — each layer opt-in rather than dumped at once.
Outcome
week-4 retention in closed beta
“I trust the plan” score, exit survey
of AI suggestions kept unedited
Reflection
The biggest lesson: with AI products, the interface is mostly trust made visible. Every rationale line and override control did more for adoption than any visual polish pass.
It also sharpened how I work with engineers — agreeing on “what the AI is allowed to decide” as a design constraint removed half of our implementation debates before they started.
More curated work
02 · 2025 · Design system
Sentra
Turning five inconsistent tools into one dashboard — and one system the team can build on.
03 · 2025 · User research
Kite
A research-led rebuild of checkout that treated drop-off as a design brief, not a metric to accept.
04 · 2024 · Branding
Atlas
One identity system stretched honestly across brand, app, and clinic-facing tools.
Interested in working together?
The block below is clickable
Contact
If this way of thinking fits your product, let's talk.
I'm open to full-time roles and collaborations — bring me a problem worth solving.