Konix

Konix

Turning Hangul into a game system for young learners.

Turning Hangul into a game system for young learners.

Turning Hangul into a game system for young learners.

Product Designer
Ages 6–12
3 learning tracks
Classroom + usability tested

THE PRODUCT INSIGHT

Korean isn't learned one letter at a time.

Learners were being asked to memorize words before understanding the system underneath them. I focused the experience on how Hangul components combine into readable syllable blocks.

Learn the system → decode new words

ㅎ

ㅎ

+

+

ㅏ

ㅏ

+

+

ㄴ

ㄴ

=

=

한

한

RESEARCH

I wasn't the learner I was designing for.

I worked with teachers and young learners to understand curriculum needs, attention patterns, and real gameplay behavior.

Teachers

Teachers

Curriculum + classroom behavior

Students 6–12

Students 6–12

Multiple skill levels

Methods

Methods

Interviews · Observation · Usability testing

DESIGN RESPONSE

Three behaviors shaped the game system.

Attention dropped quickly

Attention dropped quickly

Design response

Keep games under two minutes and provide immediate feedback.

Sound changed engagement

Sound changed engagement

Design response

Use pronunciation, voice feedback, and sound cues as part of gameplay—not decoration.

Visible progress encouraged repetition

Visible progress encouraged repetition

Design response

Reward completion and make advancement visible across sessions.

FROM CURRICULUM → GAME SYSTEM

Turning Hangul structure into an engaging learning experience

Main Game Page

Main Game Page

Game Intro Page

Game Intro Page

Block Master Game

Block Master Game

LEARNING GOAL

LEARNING GOAL

Understand how syllables are formed

GAME MECHANIC

GAME MECHANIC

Recognize + combine language components

INTERACTION

INTERACTION

Match • listen • assemble • repeat

FINAL PRODUCT

FINAL PRODUCT

6 game formats across
3 learning tracks

OUTCOME

A curriculum became a playable learning system

Analytics were specified and wired before launch, not bolted on after — completion, replay, drop-off point and error selection per game–level. That last one is the one I care about: because every distractor has a known meaning, wrong answers come back as diagnosis rather than noise.

Analytics were specified and wired before launch, not bolted on after — completion, replay, drop-off point and error selection per game–level. That last one is the one I care about: because every distractor has a known meaning, wrong answers come back as diagnosis rather than noise.

0%

0%

Game completion rate, up from the initial prototype

0%

0%

Game completion rate, up from the initial prototype

0%

0%

Game completion rate, up from the initial prototype

0%

0%

Positive feedback from teachers and parents

0%

0%

Positive feedback from teachers and parents

0%

0%

Positive feedback from teachers and parents

0

0

Game types across three content tracks

0

0

Game types across three content tracks

0

0

Game types across three content tracks

NEXT OPPORTUNITY

Adaptive learning from error patterns

The game already produces signals about which distractors learners repeatedly choose. A future adaptive layer could use those patterns to recommend the next level or extra practice.

GAMEPLAY SIGNALS

GAMEPLAY SIGNALS

repeated error patterns

repeated error patterns

AI RECOMMENDATION

AI RECOMMENDATION

suggest practice / difficulty

suggest practice / difficulty

Class-level error patterns are the most valuable thing the product generates, and currently nobody sees them. This is where I'd spend the next quarter.

LEARNER OR TEACHER

LEARNER OR TEACHER

accept · change · ignore

accept · change · ignore

Class-level error patterns are the most valuable thing the product generates, and currently nobody sees them. This is where I'd spend the next quarter.

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