KOAI (Korean AI Olympiad) is organized by KITPA and selects Korea's national team for the International Olympiad in AI (IOAI). It is a written competition testing theory and hands-on implementation across machine learning, deep learning, computer vision, and NLP. Unlike a project competition, preparation is about understanding a broad span of AI accurately and solving under time. This page shows that preparation process, in pictures.
KOAI tests not a single project but understanding and implementation across AI, through multiple-choice and short-answer questions. KITPA's official syllabus lists the four domains below, and the scope is updated each year.
Not only theory, but the ability to implement it hands-on with tools like PyTorch. Check that year's official syllabus for the domains and scope that apply per track.
That's why we build the concepts first. Cramming won't get the four domains right. Understand the concept, implement it yourself, then get fluent with the question types — in that order.
Even within KOAI, the exam format and stages differ by track. The below reflects the archived 2026 guidelines; categories, eligibility, and dates change each year, so check that year's KITPA notice before applying.
The high-school track selects the IOAI national team over three stages, so beyond the exam it also requires documents and an interview. Check which track to enter, and the eligibility and any exemptions, in that year's official guidelines.
AI knowledge isn't memorized in one pass. Understand the concept, implement it, get fluent with the question types, then practice under time. The high-school track adds documents and an interview.
Drilling problems without concepts stalls the moment a question varies. Conversely, understanding without implementing and solving by hand means running out of time. Alternating between the two is the knack.
Each domain goes beyond understanding to the level of being able to implement and explain it — because short-answer and interview questions ask "why does this work?"
The math and data handling AI needs, and the principles of classic ML like regression and classification.
Understand how neural networks learn, and build and train models yourself.
The principles and limits of image models, implementing classification and detection.
How models process text and speech, with simple implementations.
For the scope and the subjects that apply per track, see the competition page and that year's KITPA official syllabus. The syllabus is updated annually.
Multiple-choice tests precise knowledge; short-answer tests the ability to explain why. With the same answer, showing the reasoning is what earns the points.
Check readiness on five axes. Knowing concepts but unable to implement, or fast at solving but unable to explain, stalls on short-answer and interview. Strong prep fills all five evenly.
The five axes are concepts · coding · solving speed · explanation · syllabus coverage. A diagnostic finds the weak axis and fills it first.
Count back from the written-exam date in that year's KITPA notice. The red and gold bands below are concepts and implementation, the green is question types and mock exams, and the last is (high-school) documents and interview.
Fit school exams and assignments first, then set the prep schedule. The concept and implementation bands overlap and repeat with the practice band. Count back the exact written date from that year's KITPA notice.
This prep is not competition-only memorization. Understanding how AI works and being able to implement and explain it carry straight over to later AI study, projects, and university coursework.
Learning to explain why, not just memorize formulas.
Turning theory into PyTorch code and actually training and validating.
Communicating not just the result but the reasoning, in writing and speech.
Practicing to work a broad syllabus calmly within a set time.
There is little you need to do at home. Listen to which parts of AI spark your child's curiosity, and support steady study time.
Awards are not guaranteed. Categories, eligibility, dates, the tested scope, and IOAI
national-team selection differ each year, so always check that year's KITPA official notice
(kitpa.org). Korean universities may restrict external-competition records depending on the track,
participating/advancing/winning does not guarantee admission, and IOAI national-team selection is a
separate track from admissions. For the competition overview see the
competition page; for questions, contact your teacher or
jc@citcoding.com. Same approach for other competitions:
CAC ·
Technovation ·
KSEF.