
ZEP Quiz
NAVER Z CORP
Role
Task Owner
Lead Product Designer
Timeline
7 Months
(Feb – Aug 2025)
Team
NAVER Z Corp.
ZEP Quiz AI TF
Paid-user usage
within 2 months of launch
Avg. chat messages
sent per student per quiz
Reuse rate
teachers set the feature again.

Problem Space
Content Learning
Quiz Session
Result Check
Review & Feedback
Field research

In their words – verbatim from field research
Asking means exposure.
Verbatim quote from a student interview – what they said about asking questions after a quiz. Korean original is fine.
Review time doesn't exist.
Verbatim quote from a teacher interview – what they said about review time or individual follow-up.
Thirty classmates are watching.
Verbatim quote that captures the fear of judgment in front of 30+ classmates.
“I can't get to every student.”
Teachers face 30+ students at once and cannot address individual misunderstandings in real time.
“I just move on.”
After quizzes, most students never ask about their incorrect answers.
“I have to teach to one level.”
Classrooms hold wide differences in understanding, but the lesson can't.



The Idea
How Might We
Three pillars of the companion concept
Rapport before review
Students open up about confusion only after they trust the assistant. Trust is built during the quiz, not after it.
A friend, not a teacher
Asking for help should feel like talking to a companion, not raising a hand in front of 30 classmates.
Reflection embedded in the flow
Review can't be a separate destination students must choose to visit. It has to live where the quiz already is.
Solution
The Experience
Outcome Brief
AI Pet Companions
Six companions who cheer students through the live quiz and earn their trust.
Student Review Chat
A private 1:1 space where students revisit their own mistakes.
Teacher Insight View
A window into the misunderstandings a class never says out loud.
Part 1
Personalites
AI review chat, in action
Drop a short looping screen recording here – phone ratio (9:16), autoplay + muted + loop. Show the pet responding to a wrong answer.
Part 2
User Goals & Features
Private 1:1 Space
Students revisit missed questions in a private chat, anytime – no audience, no judgment.
Personal Explanations
Feedback is generated from each student's own written reasoning, not a generic answer key.
Embedded in the Flow
Review lives where the quiz already is, not in a separate destination students must choose to visit.
My Score screen
AI chat thread
Part 3
User Goals & Features
Individual Gaps
Teachers see each student's learning gaps and misunderstanding patterns through chat details.
Class-Wide Patterns
When multiple students share the same misconception, the pattern surfaces across the full chat history.
Next-Lesson Adjustment
Teachers use what the class could not say out loud to adjust the next lesson.
Teacher LMS
with chat history list
Image needed
In the wild – a teacher running the review flow in class
Outcomes
After user testing, the assistant shipped as part of the ZEP Quiz Premium plan.
Paid-user usage
Within 2 months of launch.
Reuse rate
Used the feature again.
Avg. chat messages
Sent per student on the page where the feature was used.
How I validated it
Prompt evals in LangSmith
50+ prompt versions scored against real student answers – quality measured, not assumed.
Live classroom pilot
Students used the assistant in real quizzes during the pilot – behavior observed in the classroom, not in a lab.
Teacher feedback
Pilot teachers reviewed the insight view and shared what changed in their classrooms.
What we observed
Active engagement
Students who normally stayed passive became active participants, voluntarily opening the review chat after quizzes.
Deep immersion
High focus maintained through personalized AI interaction.
Psychological safety
The private, non-judgmental chat lowered the emotional barrier to asking questions.







[Number needed]
Piloted in [classrooms / students / schools / duration].
Reflections
High student engagement vs. fear of replacement.
AI can lower the emotional cost of asking questions.
A private, non-judgmental space mattered as much as the correctness of the explanations.
Next Project
Case study · NAVER Z · 2026
2026 Cora Lee
cora
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