CIT focuses on understanding, designing and validating AI rather than merely operating tools. Students may begin without programming experience, but the coding required depends on the question, and they must understand and explain every tool, line of code, dataset and analysis used in the final work. Biology, humanities, art and other non-CS interests can become a distinct AI+X extension.
For international-school students: protect AP, IB, and IGCSE academics first, then extend a separate student-owned project through Apgujeong or online lessons. International-school AI education & EC portfolio →
For science or gifted-school preparation: do not assume that every school accepts a common portfolio submission. Check the separate official-rules and inquiry-evidence route →
Students must understand and explain every tool, AI output, line of code, and analysis used. Competition entry, awards, and admissions outcomes are not guaranteed. | Last reviewed: August 30, 2026
Yes. We begin with the student's subject interest and question, then teach the data, statistics and programming needed to test it. In the final work, the student must explain the libraries and AI tools used, the code, data provenance, errors and limitations.
AI + X is an approach to designing meaningful projects at the intersection of a student's own field of interest (X) and AI technology. Below are example combinations covered in actual classes.
| Field of Interest (X) | AI Project Direction | Tools Used |
|---|---|---|
| Life sciences | Approved public-data medical-image classification or biological pattern comparison | Python, TensorFlow, public data |
| Humanities / social sciences | Public-text sentiment study, education tool or policy-data exploration | NLP, Pandas, scikit-learn |
| Economics / business | Public economic-data model or interactive policy simulator | Data analysis, regression models |
| Art / music | Music-pattern classifier or accessibility tool with documented rights and data provenance | MIDI data, explainable AI tools |
| Environment / geography | Model a local issue with satellite, climate or municipal public data | CNN, GIS, public data |
No example matches every selected filter. Broaden the filters or request a diagnostic map.
Students may begin with no Python experience. The prerequisite phase varies with the student's starting point and project scope, and the student must explain every library and tool used in the final work. For more details, see the AI Portfolio Program page.
An AI project is not an admissions object. It is an evidence system for student-owned work: dates, role, question, new contribution, methods, data provenance, results, limitations, iterations and mentor support. CIT uses the following design stages.
Students do not need to imitate a particular outside program. They need a question grounded in their subject interests, new evidence, an exact student role, and an outcome they can explain and defend. Selective colleges do not publish one required project duration, count, or competition formula.
This principle runs throughout the CIT curriculum. Iterating and validating one topic, then documenting related papers, presentations or competition artifacts, can show the development of the same student-owned project without inflating each artifact into a separate activity. If you're interested in advanced tracks, see the Agentic Engineering course.
CIT's AI education can start as early as preschool or 1st grade. That's because the starting point of AI education isn't 'how to use AI tools' but 'how to think.' In preschool and the lower elementary grades, students don't learn AI directly; instead, they firmly build the problem-solving skills that form the foundation of all AI learning, through play and activities.
Before working with AI directly, students intensively train the following three thinking skills.
At CIT, we believe the essence of AI education is not acquiring skills but 'expanding how you think'. Students experience just how far they can stretch their own thinking, and learn how to turn that thinking into reality through AI.
From the upper elementary grades, once problem-solving fundamentals are in place, it flows naturally into AI literacy; middle schoolers move on to Python data analysis and basic machine learning; and high schoolers progress to full AI project design and competition prep. We use a placement test to guide each student to the best starting point for their age and level.
CIT's AI curriculum is structured in five stagesmatched to a student's stage of cognitive development. It starts from problem-solving fundamentals and progresses through AI literacy, data analysis, and machine learning to AI projects.
| Steps | Recommended for | Core Learning |
|---|---|---|
| ① Problem-Solving Fundamentals | Preschool · lower elementary | Problem decomposition, logical and mathematical thinking (no coding or AI) |
| ② AI Literacy | Upper elementary | Understanding how AI works, its history, and real cases; a critical perspective (no coding) |
| ③ Data Analysis | Middle schoolers | Collecting, cleaning, and visualizing data with Python·Pandas |
| ④ Machine Learning | Middle and high schoolers | Supervised and unsupervised learning; building and evaluating scikit-learn models |
| ⑤ AI Project | High schoolers | Topic → design → presentation → evidence record; review eligible project-output routes |
Starts in preschool and the lower elementary grades. Before working with AI directly, students build decompositional thinking that breaks big problems into small ones, logical thinking that finds order and rules, and mathematical thinking that solves with numbers and patterns: all through play and activities. This is the stage that forms the foundation of all AI learning.
Students explore what AI is, its history and principles, and everyday examples of AI. They understand AI concepts without coding and form a critical perspective on AI. Begins in the upper elementary grades, once problem-solving fundamentals are in place.
Students learn to collect, clean, and visualize real data using Python and Pandas. They build the ability to spot patterns in data and draw out meaningful insights.
Students understand the concepts of supervised learning, unsupervised learning, classification, and regression, and build real machine learning models with scikit-learn. They experience evaluating and improving a model's accuracy.
Students choose a topic they care about and carry out the full process from data collection through model design and presentation. The process becomes a student-owned evidence packet that may be adapted only for competitions the student is eligible to enter and application fields that permit it. It does not automatically become an admissions EC or competition entry. For details, see the Portfolio Program page.
Data analysis, statistics, logical reasoning and scientific method can deepen related coursework. They do not automatically improve grades or admissions outcomes, and CIT protects academic deadlines and student authorship first.
These are illustrative learning directions, not claims about student outcomes or admissions formulas. Useful evidence starts with a concrete question, adds a student-owned contribution and baseline comparison, and records errors, limitations, and the exact student role.
Project experience becomes useful evidence when the student owns the work and can explain every decision. In a permitted Common App or other activity field, state the student's exact role, dates, outcome, and mentor support truthfully and do not assume external-link review.
Yes, you can. Alongside in-person classes in Apgujeong, CIT runs Online 1:1 or small-group (1:n) AI mentorship for Korean students living abroad, in places like Singapore, Hong Kong, and the U.S. East Coast. Online isn't an alternative to in-person but an expanded way to access it. The same curriculum and the same caliber of mentor are assigned to fit your time zone.
Class schedules can be coordinated to the Singapore (SGT UTC+8), Hong Kong (HKT UTC+8), and U.S. East Coast (ET UTC-4/5) time zones. Classes run as 1:1 video sessions (Zoom·Google Meet), and session recordings are provided to support review.
If you're planning to return to Korea, you can start online first and then transition naturally to in-person classes at our Apgujeong campus once you're back. For details, see the online class guide page or reach out directly via KakaoTalk.
| Region of residence | Recommended class times (KST) | Main tracks used |
|---|---|---|
| Singapore (SGT) | 8-10 PM KST (7-9 PM local) | AI+X portfolio, AI Olympiad prep |
| Hong Kong (HKT) | 8-10 PM KST (7-9 PM local) | AI+X portfolio, KSEF research mentorship |
| U.S. East Coast (ET) | 7-9 AM KST (6-8 PM ET, previous evening) | AI+X portfolio, head-start prep before returning to Korea |
CIT connects a simple early idea to the student's interests and develops it into a single-topic, in-depth project where they can explain "why AI is the right tool for this problem." It's the strength of your explanation and coherence (not the number of deliverables) that sets a student apart.
"I really appreciated how you expanded my child's simple initial idea all the way into an 'AI debate tool.' My child has always been interested in debate and social issues, and seeing those interests connect so naturally with the project, I think the polish and distinctiveness will be far greater."
Students can start as early as preschool or 1st grade. At this stage, rather than working with AI directly, they first build the foundations for AI learning: decompositional thinking that breaks problems into smaller parts, and logical and mathematical problem-solving. The AI Literacy course begins once those foundations are in place, from the upper elementary grades.
Students may begin without programming experience. The coding required depends on the project, and students must understand and explain every tool, AI output, line of code, and analysis used in the final work. We do not submit code or content the student cannot defend.
Yes. Students begin with a subject question and learn the coding, data collection, analysis, interpretation and presentation required for that project. The coding level varies by project, and the student must explain every tool and result in the final work.
The process is: ① connect a subject interest with AI → ② record the boundary from assessed work → ③ define a new question, dataset, user, method, or outcome → ④ implement and analyze the work independently → ⑤ document results, limitations, data provenance, and student ownership → ⑥ adapt it only to competitions, school activities, service, or portfolios permitted by the current rules and admissions route.
There is no single required project. Start with a concrete question from life sciences, humanities, economics, art, or another genuine interest, then verify that the data may be used and compare against a non-AI baseline. Record the student's new contribution, code and experiment log, results, errors, limitations, data provenance, and the scope of mentor or AI support. The student must be able to explain every choice.
We use generative AI tools (ChatGPT, Claude, etc.), but the goal is to build students' ability to understand how AI works and evaluate it critically. We aim for the kind of thinking that lets them judge for themselves "why what the AI says is right, and why it might be wrong."
Data analysis, statistics, logical thinking and experimental design can support deeper understanding of related math and science coursework. Grades are not guaranteed, and IB or AP assessed work remains student-authored and separate from an independent project.
KSEF (Korea Science & Engineering Fair), Technovation Girls, CAC (Congressional App Challenge), and some AI hackathons are project-output options. Before applying, check the current official rules for age or grade, residence or regional eligibility, team composition, reuse of prior work, and advancement. USAAIO is a separate theory-and-coding exam track. Its 2027 Round 1 is a three-hour exam whose coding tasks must be completed in Google Colab, proctored onsite at a school or authorized site. It is not an online-at-home qualifier. Verify participant and Team USA eligibility separately. See the USAAIO competition prep page for that boundary. Entry and awards are not guaranteed.
Yes. We run one-on-one online AI mentorships scheduled around your time zone for Korean students living in Singapore, Hong Kong, and the U.S. East Coast. They're held at the same level as our in-person Apgujeong curriculum, and you begin after a consultation via KakaoTalk or phone and a placement test. For details, see the Online Class Guide page.
Yes, but keep the records separate. For 2027, USAAIO Round 1 is a three-hour exam whose coding tasks must be completed in Google Colab, proctored onsite at a school or authorized site. It is not an online-at-home qualifier. The official season page calls Round 1 “open to everyone,” while the Contest Rules list U.S. and Canadian citizenship, residency, or school-enrollment categories. Students outside those categories should ask the organizer before registering, and Team USA eligibility is separate. An AI portfolio project and Olympiad math and ML theory preparation may run in parallel, but each remains governed by its own competition and school rules.
Not sure where to start with AI education? Through a placement test and free consultation, we'll design the AI learning path that fits your child. Families living overseas can have the same consultation online.
ACADEMIC → INDEPENDENT EXTENSION → VERIFIABLE EC
Protect grades and required school submissions first. Then branch into a new, student-owned question and document evidence that the applicable route permits.
Course knowledge + new question + new evidence + student ownership + verification = defensible EC
CIT helps students extend what they have learned into new work. We do not duplicate assessed submissions, write school coursework for students, or present old work as a new competition project. The student must make the decisions, create the work, and be able to explain every part.
CIT does not duplicate assessed submissions, write student coursework, or guarantee awards, international selection, or admission.