College application research evidence · understanding and original contribution · Seoul and online
Project courseThe student keeps the work and the record they made.
AI + Sports: Play Data Lab
Grades 3-5: success rates in practice logs, how graph...
- Free consultation and level check
- Apgujeong classroom or online
- One-to-one or small group
- Registered academy no. 11882
Is AI + Sports: Play Data Lab a good fit for elementary students in Grades 3-5?
AI + Sports: Play Data Lab is a good fit for students in grades 3-5 who are interested in success rates in practice logs and how graph axes shape impressions. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make an anonymous play log with two sports graphs, a rep counter and pose comparison chart, and a sports prototype with a 3-minute demo.
Students in Grades 3-5 choose one countable question from a favorite sport, log each practice as one row, and divide successes by attempts to compare success rates on the same basis. They chart data in bar and line graphs, record micro:bit acceleration, and then compare an elbow-angle rule with a small pose classifier on samples it has not seen. No names, weights, or heart rates are collected, pose work uses stick figures and face-covered samples, and results are never read as health or skill judgments. The student sets their own question, then designs and tests a feature, model, interface, or solution of their own. In an application or interview, the student separates the material they drew on from their own decisions, results, failed attempts, revisions, and limits.
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As soon as my child got home, they showed me the game they had made. They said it was so much fun.
- Age group
- Grades 3-5
- Academic subject
- AI & DataBrowse subject
- Course type
- Course
- Format
- Online or Apgujeong in person · one-to-one or small group
- Teaching language
- Korean by default, with complete English materials
- Curriculum status
- Lesson-readyEvery learning item includes the evidence, worked example, practical work, and tutor guidance required by CIT's lesson-ready standard.
From open research to student-owned work
Students inspect a relevant public source, reproduce the idea, then add an original question, feature, or test. Every source is public, and each lesson names the exact page it opens.
How can I explain this course to my child?
If questions about success rates in practice logs or how graph axes shape impressions keep making you ask why, AI + Sports: Play Data Lab lets you investigate the question with evidence, then build and defend an extension of your own.
Which interests suggest this course?
- success rates in practice logs
- how graph axes shape impressions
- elbow angles from pose points
What does the student finish?An anonymous play log with two sports graphs, a rep counter and pose comparison chart, and a sports prototype with a 3-minute demo.
Is this a good fit?
A strong fit for students who want to understand, build, test, or responsibly use AI systems.
Beginners can start with visual tools.
Students should be ready to save work and explain a simple choice.
Text coding is introduced only when it supports the project.
What will my child learn?
- Log one practice per row and divide successes by attempts so every session is compared on the same basis.
- Compare a graph whose axis starts at zero with a cropped one and explain the different impressions they give.
- Write a rule that counts one repetition when acceleration crosses a set value, and tune the wait time to stop double counts.
- Compare an elbow-angle rule with a small pose classifier on the same new samples to decide whether AI is needed.
How does the course progress?
- 1Sports Questions and Safe Data
Set a countable question and what not to build, calculate success rates from an 8-row anonymous log, and check units in salary and injury tables.
- 2Seeing Movement as Numbers
Draw two graphs, record acceleration values, and count repetitions with a threshold rule.
- 3Testing Pose AI on a Small Scale
Find shoulder, elbow, and wrist points, test an angle rule against a small classifier, and record six pose failures without keeping video.
- 4Play Data Capstone
Pick a graph dashboard, motion counter, or pose demo, then test it on six normal, edge, and failure cases and demo it.
What counts as useful evidence?
An anonymous play log with two sports graphs, a rep counter and pose comparison chart, and a sports prototype with a 3-minute demo.
What should an admissions reader be able to see?
The student sets their own question, then designs and tests a feature, model, interface, or solution of their own. In an application or interview, the student separates the material they drew on from their own decisions, results, failed attempts, revisions, and limits.
Useful evidence may include a documented dataset, baseline comparison, model evaluation, error analysis, and a clear record of the student's own decisions.
The evidence an application reads is what the student understood, completed, and can explain.
Which sources were reviewed?
Catalog reviewed: 2026-10-03
Questions parents search before choosing this course
Is AI + Sports: Play Data Lab a good fit for elementary students in Grades 3-5?
AI + Sports: Play Data Lab is a good fit for students in grades 3-5 who are interested in success rates in practice logs and how graph axes shape impressions. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make an anonymous play log with two sports graphs, a rep counter and pose comparison chart, and a sports prototype with a 3-minute demo.
Can my child take AI + Sports: Play Data Lab online or in person, one-to-one or in a small group?
Yes. CIT offers online and in-person lessons at its Apgujeong academy in Gangnam, Seoul, with one-to-one and small-group options. A readiness consultation confirms the available format and starting point for the course.
Does my child need prior subject knowledge or coding experience for AI + Sports: Play Data Lab?
Beginners can start with visual tools. Students should be ready to save work and explain a simple choice. Text coding is introduced only when it supports the project.
What will my child make or practice in AI + Sports: Play Data Lab?
The main evidence is an anonymous play log with two sports graphs, a rep counter and pose comparison chart, and a sports prototype with a 3-minute demo. Students also document decisions, tests, feedback, and limits in age-appropriate language.
How can a student describe work from AI + Sports: Play Data Lab in a college application?
The student sets their own question, then designs and tests a feature, model, interface, or solution of their own. In an application or interview, the student separates the material they drew on from their own decisions, results, failed attempts, revisions, and limits. Useful evidence may include a documented dataset, baseline comparison, model evaluation, error analysis, and a clear record of the student's own decisions.
How are the schedule and tuition for AI + Sports: Play Data Lab determined?
CIT confirms the student's readiness, goal, location, class size, and current availability before recommending a course plan. The consultation and level check are free; tuition is explained before enrollment.
When should a student start?
There is no fixed intake month. CIT reviews the student's current school term, readiness, and available hours, then names the point in the course where they should begin.
I worried because it was hard to explain to my child why they should learn coding. Being with friends seems to make the classes enjoyable for them.
Where else parents check CIT
Choose the course after a readiness check
CIT can compare this course with nearby options by age, subject, and current preparation.