AI education at CIT Coding Academy focuses not on how to use AI tools, but on building the ability to understand how AI works and to design it yourself. With a five-stage curriculum — problem-solving fundamentals (decomposition · logical · mathematical thinking) → AI literacy → data analysis → building ML models → AI project design — students from preschool and the lower elementary grades up through high school can learn at their own level. For students planning to major outside CS — in biology, the humanities, the arts, and more — we also run an AI + X Interdisciplinary Track. 5-minute walk from Apgujeong Station; families in Singapore, Hong Kong, and the U.S. East Coast can join via 1:1 online mentorship.
Published: March 12, 2026 | Last updated: May 27, 2026
Yes, you can. The reason AI portfolios (extracurricular activities / ECA) draw attention in U.S. college admissions isn't "how well you write code" but "what problem you tried to solve with AI". Starting from this point, CIT runs a separate AI + X Interdisciplinary Trackfor students who don't plan to major in coding — in biology, the humanities, business, the arts, and more.
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 / Pre-med | Medical image classification, pattern analysis of genomic data | Python, TensorFlow, public medical datasets |
| Humanities/social sciences | Social media sentiment analysis, news bias detection, predicting policy outcomes | NLP, Pandas, scikit-learn |
| Economics · Business | Stock price pattern analysis, consumer behavior prediction models | Data analysis, regression models |
| Art · Music | Fine-tuning image-generation models, music pattern classification | Stable Diffusion, MIDI data |
| Environment/earth sciences | Detecting deforestation from satellite imagery, predicting climate data | CNN, public satellite data |
It's fine to start with no Python at all. The first 4–6 weeks focus on reading, interpreting, and visualizing data, using code at the level of already-proven libraries. the ability to design 'what to ask' with AIneeds to be in place before coding skill. For more details, see the AI Portfolio Program pageas well.
In U.S. college admissions, an AI portfolio has to show up concretely in the Common App activities section and essays. Here are the portfolio design steps CIT actually uses.
These are the programs that serve as benchmarks for AI activities in U.S. college admissions. The Stanford AIMI (AI in Medicine & Imaging) in-year internship, Inspirit AI, Veritas AI, and others have become leading external programs for high school AI portfolio building. Stanford Pre-Collegiate and the Harvard AI Bootcamp are recruiting their summer 2026 cohorts. The principle these programs all emphasize is the 'spike model' — digging deep into one field for 6+ months carries far more weight at selective colleges than touching on several fields shallowly. (Source: Stanford AIMI / Inspirit AI)
The principle that depth matters more than breadth runs throughout the CIT curriculum. Developing one topic across multiple semesters and carrying it into papers, competitions, and presentations builds a far clearer admissions story than a collection of several short-term projects. If you're interested in advanced tracks, check out the Agentic Engineering courseas well.
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 selection → design → presentation; portfolio and KSEF competitions |
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 entire process, from data collection through model design to presenting results. It can be used as a portfolio and connects to competition entries like KSEF and admissions EC activities (extracurricular activities). For details, see the Portfolio Program page.
AI education connects directly to both school subjects and admissions. That's because students internalize statistics and mathematical thinking through data analysis, and logical reasoning and scientific methodology through model design.
CIT students design and build a variety of AI projects themselves, by grade level. A project that starts from a concrete question at the intersection of your own interests and AIis the most convincing in admissions. Here are some representative examples.
Project experience becomes part of a student's record (portfolio) and is listed in concrete detail on the U.S. college Common App activities section. Following the spike model, digging deep into one topic is more advantageous than several shallow projects.
Yes, you can. Alongside in-person classes in Apgujeong, CIT runs 1:1 online AI mentorshipfor 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 online class guide pageor 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."
This reflects one student's individual experience and does not guarantee the same results. · Last reviewed 2026-05
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.
At the foundational level, students learn AI concepts without coding. The advanced levels (③ Data Analysis and beyond) use Python, but even students who start with no coding experience learn it from the basics within class. In particular, the AI+X track is designed so that non-CS students with no Python knowledge at all can get started.
Yes. CIT's AI+X track is designed for students who don't intend to major in coding—those in biology, the humanities, business, the arts, and more. Rather than Python coding, it focuses on "which question should I solve with AI," while building skills in data collection and analysis, results interpretation, and presentation. The core of this track is building U.S. college admissions ECAs (extracurricular activities) out of AI projects on topics like biomedicine, psychology, social science, and the arts.
The process runs as follows: ① find the intersection of your interest and AI → ② explore public datasets and design a hypothesis → ③ implement a model with Python·scikit-learn or no-code AI tools → ④ analyze and visualize the results → ⑤ organize it on GitHub or a portfolio site → ⑥ enter it in a competition (KSEF·USAAIO, etc.) or your Common App activities section. Following the "spike" model, digging into a single topic for six months or more is far more advantageous in U.S. admissions than several shallow projects.
The most compelling projects solve a real problem at the intersection of your own field of interest and AI. If you're interested in life sciences, that could be medical image classification or genomic data pattern analysis; for the humanities and social sciences, social media sentiment analysis or predicting policy outcomes; for the arts, fine-tuning an image generation model or analyzing musical patterns. What matters is the reasoning that lets you explain "why you applied AI to this particular problem."
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."
Training in data analysis, statistics, and logical thinking has a positive effect on math and science learning. In particular, statistical thinking and experimental design concepts connect directly to IB and AP science coursework (IAs, etc.).
Students can enter KSEF (Korea Science and Engineering Fair), Technovation Girls, CAC, AI hackathons, and more. If you're interested in the AI Olympiad track, prep for USAAIO (USA AI Olympiad) connects through a separate course. For competition schedules and prep roadmaps, see the USAAIO competition prep page.
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. Note, however, that the USAAIO (USA AI Olympiad) is a competition for U.S. and Canadian citizens or residents, so students based in Korea would aim instead for the national team selection route to the IOAI (International Olympiad in Artificial Intelligence). AI portfolio projects and Olympiad math·ML theory prep can be designed to run in parallel within the curriculum. For details on the competition track, see the USAAIO prep page.
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.