Emotional experienceEmotional
Comfort, trust, confidence, frustration, and enjoyment
Design ratings, interviews, and adjective pairsCollege application research evidenceGrades 3-12 · 36 missions
Understand how university researchers test technology.
Build evidence colleges can understand.
Students learn how researchers ask whether an app, product, robot, or AI is easy, useful, fair, and safe. Younger students build the foundation by questioning, comparing, and explaining. Older students preserve their own question, design choice, result, revision, and limits for an authentic college application or interview. Choose online or in-person lessons in Apgujeong, Seoul, in a one-to-one or small-group format.
Important boundaryThis course improves designs. It does not judge, diagnose, or try to control a person. A course title does not guarantee admission.
- 36+4complete missions
- 6experience lenses
- 1-4students per class
01 / Beyond emotion
Study the whole experience,
not one emotion.
Emotion is one part. Students also study thinking, senses and movement, actions, social context, and what changes over time.
Thinking experienceCognitive
Attention, understanding, memory, workload, and decisions
Time, recall, errors, and a short workload ratingSensory and physical experienceSensory & physical
Colour, sound, texture, brightness, motion, and ergonomics
Preference, reach, comfort, and response timeAction experienceBehavioural
Actions, pauses, task flow, stopping, and recovery
Clicks, mistakes, completion, and observation notesSocial experienceSocial
Inclusion, communication, teamwork, and access barriers
Observation, interviews, participation, and access checksTime and contextTemporal & contextual
What happens before, during, and after an interaction
Journey maps, touchpoints, and short activity logs02 / Engineering cycle
Move from a nice idea
to testable engineering.
Observe a real task, make one clear change, compare two versions, and use the evidence to improve.
- 01Observe
- 02Ask
- 03Map
- 04Define
- 05Translate
- 06Build
- 07Test
- 08Audit
- 09Improve
03 / Try the evidence
Try the difference.
Then read the explanation.
These demos use fixed synthetic examples. They do not save input or call an outside model.
01
Mood Mixer
Change one variable and compare how the screen design feels.
Nova SchoolReady to start the next activity?
This version feels more...
Responses change with the person and context, so a real test matters.
02
Notification Storm
Tap 1 to 6 in order while synthetic alerts interrupt the task.
Synthetic alerts will appear here after you start.
0 interruptions0 errors- time
03
Human vs. AI Label
Judge a fixed synthetic sentence, then compare human and model labels.
1 / 5
No free text · no outside model · no saved response
04 / Three-level pathway
Start by making.
Grow into research and responsible AI.
Every level uses the same clear loop: observe, set a requirement, build, test, and improve.
Level 1 · Grades 3-5
Design Detective Lab
Emotion & Sensory Experience DesignStudents spot useful design clues, make two versions, test them, and improve the design without judging the person.
- Classes
- 12 × 75 min
- Focus
- Notice, make, and compare.
Level 2 · Grades 6-8
UX Mission Lab
Human-Centered UX & Experience EngineeringStudents study a user journey, set one clear design rule, and run a fair A/B usability test.
- Classes
- 12 × 90 min
- Focus
- Research, build, and test.
Level 3 · Grades 9-12
Human-AI Research Lab
Human Experience Engineering & Affective AIStudents build a study or system that shows uncertainty, checks bias, protects user choice, and explains its limits.
- Classes
- 12 × 120 min
- Focus
- Measure, model, and audit.
05 / Project evidence
Show the process,
not only the final screen.
Each project includes a user, journey, requirements, two versions, a test, an improvement, and an ethics check.
25 projects
Elementary · Physical
Welcome Buddy · Calm Transition Companion
It is hard to predict what will happen next or what to do first.
See process and evidence →Middle school · Interactive
FocusFlow Homework Starter Experience
Too much information appears before the first action, so the starting path gets lost.
See process and evidence →High school · Interactive
Human-Friendly Adaptive Study Assistant
The system does not show why it made a recommendation, and the user cannot easily correct it.
See process and evidence →Elementary · Interactive
Friendly School Navigation
The signs do not clearly connect to the next action.
See process and evidence →Elementary · Interactive
User-Controlled Practice Game
Feedback is too frequent or only comes in one form.
See process and evidence →Elementary · Physical
Classroom Choice Controller
The user cannot change the environment settings directly.
See process and evidence →Elementary · Physical
Kind Robot Companion
The robot's motion and sound happen without enough warning.
See process and evidence →Elementary · Interactive
Error Message Monster Makeover
A scary or vague message does not explain the next action.
See process and evidence →Elementary · Interactive
First-Five-Minutes Game Guide
The goal and recovery path are hard to find.
See process and evidence →Middle school · Interactive
알림 방식에 따른 사용 편의 비교하기
Every alert interrupts in the same way.
See process and evidence →Middle school · Interactive
Accessible Game Onboarding
Dense information and single-sense instructions block some players.
See process and evidence →Middle school · Interactive
Inclusive Presentation Practice
Unwanted intensity and public comparison create pressure.
See process and evidence →Middle school · Interactive
Privacy-Preserving Information Kiosk
The kiosk asks for a personal reason before giving help.
See process and evidence →Middle school · Physical
사람이 다음 행동을 알 수 있는 로봇 화면 만들기
Speed, distance, and status are not visible enough to predict what comes next.
See process and evidence →Middle school · Interactive
School Portal Error Recovery
The cause of the error and whether work was saved are unclear.
See process and evidence →Middle school · Interactive
Accessible Club Registration
Too many choices and unclear status after applying cause users to leave.
See process and evidence →Middle school · Interactive
Trustworthy AI Failure Explanation
The system gives no useful next step when the AI is wrong or does not know.
See process and evidence →High school · Interactive
Kansei Product Experience Optimisation
The link between design choices and trust, comfort, or clarity has not been tested.
See process and evidence →High school · Interactive
감정 자료를 나누는 AI의 편향과 한계 확인
Average performance hides failures in specific contexts and disagreement between people.
See process and evidence →High school · Interactive
User-Controlled Adaptive Game
The game guesses the player's state, changes the level, and does not explain why.
See process and evidence →High school · Interactive
Human-Friendly AI Assistant
The model gives a definite answer even when the context is unclear.
See process and evidence →High school · Interactive
Responsible Notification Engine
Optimising engagement increases interruption and false urgency.
See process and evidence →High school · Physical
Human-Robot Handoff Experience
We do not yet know how distance, speed, timing, and feedback affect safety and trust.
See process and evidence →High school · Interactive
AI Explanation & Trust Calibration Study
A long or overconfident explanation can make trust less accurate.
See process and evidence →High school · Interactive
Multilingual Sentiment Limitations Study
People and models often disagree on sarcasm, indirect language, and mixed-language text.
See process and evidence →06 / Hands-on classes
Make, test, and improve
inside one class.
Each mission starts with a short problem. Most class time goes to investigating, building, testing, and explaining.
화면 조건 하나를 바꿔 인상 비교하기
Students complete Mood Mixer Machine, make one clear design choice, test it, and record what changed.
Output
Design notes, Prototype or test plan
Evidence
One design choice, one task result, and one next change
Notification DJ Usability Test Lab
Students complete Notification DJ, make one clear design choice, test it, and record what changed.
Output
Design notes, Prototype or test plan
Evidence
One design choice, one task result, and one next change
사람과 AI의 문장 판단 차이 분석하기
Students complete Sentiment Model on Trial, make one clear design choice, test it, and record what changed.
Output
Design notes, Prototype or test plan
Evidence
One design choice, one task result, and one next change
07 / Visible outcomes
The student's decisions
become the portfolio.
We grade how a student studied and improved a problem, not what kind of person they are.
08 / Safety by design
Keep people in control.
Use safe tasks, minimal data, clear limits, and a real way to change or stop the system.
No emotion diagnosis
Do not claim to know a person's feelings, personality, health, or ability.
Design, not the person
Measure how a design supports a task. Do not score the user.
Choice and opt-out
Users can change, stop, correct, or refuse an adaptive feature.
Minimum data
Use synthetic or anonymous task data and collect only what the activity needs.
No hidden recording
Do not secretly collect voice, face, account, message, or sensor data.
Accessible alternatives
Offer more than one way to see, hear, control, and complete a task.
Limits stay visible
State what a small test or model cannot prove and what should be checked next.
09 / Family FAQ
What parents search
before class.
Clear answers about age fit, location, online lessons, student work, coding, AI limits, and privacy.
What is Emotion & Experience Engineering?
Students study how people understand, feel, act, and interact. They turn that evidence into clear design rules for products, services, robots, and AI.
Is this one established school subject?
It is a K-12 program that connects established methods from Kansei engineering, human factors, UX and HCI, and affective AI.
Does the course judge or change a child's emotions?
No. Students improve a design, not a person. They never need to share private feelings or personal stories.
Can elementary students do this?
Yes. They start with one visible variable, such as colour, sound, shape, motion, or order, and test two simple versions.
How does this connect to coding?
Students turn a user need into a feature, build it with paper, Figma, Scratch, web code, Python, or robotics, and test the result.
Does affective AI know how someone feels?
No. A model only estimates patterns from limited input. Students learn to show uncertainty, ask, correct, abstain, and stop.
What do students make?
Examples include a welcome guide, a quiet notification system, an error-recovery app, a robot interface, and an AI limits audit.
How do you protect privacy?
We prefer synthetic data, prohibit hidden recording and diagnosis, collect as little as possible, and always provide a way to opt out.
Is Emotion & Experience Engineering available online or in person, one-to-one or in small groups?
Yes. CIT offers online and in-person lessons at its Apgujeong academy in Gangnam, Seoul, with one-to-one and small-group options. A free consultation confirms the available format, starting level, and schedule.
Course consultation
Turn a student's interest
into an experience project.
We can match the grade, coding experience, and topic to paper, Figma, Scratch, web, Python, or robotics.