A portfolio is not an admissions object with a required project count. It is an evidence system for work the student owns and can explain: dates, role, question, new contribution, methods, student-authored code, data provenance, results, limitations, iterations, verification, artifacts, and mentor disclosure. Students may begin without programming experience, but they must understand and explain the tools and code used in the final work.
01 · International-school AI education and EC portfolio Protect AP, IB, and IGCSE academics and exam schedules before extending into independent work. Open the international-school route →
02 · Science and gifted-school AI portfolio CIT does not imply one AI portfolio submission shared by every school. The canonical guide covers registered names and search aliases for 22 current science high schools and 8 gifted schools. It lists planned (가칭)시흥과학고 (Siheung Science High School), (가칭)이천과학고 (Icheon Science High School), KAIST-affiliated Chungbuk AI·BIO Gifted School, and GIST-affiliated AI Gifted School outside the current 30-school count. Lessons are available online nationwide or overseas. Check school names and official-route evidence rules →
CIT does not assume a project count, duration, or competition record shared by every route. Check the current official rules before submission. Last reviewed: August 30, 2026.
A portfolio records what a student built, the role they played, the evidence behind the result, and what they learned. Whether it may be submitted in an application depends on the university and route, but a well-documented process also supports learning, interviews, collaboration, and project handoff.
A list of finished outputs does not establish what a student contributed. The student records why the question was chosen, their exact role, iterations, technical choices, data provenance, results, and limitations. CIT provides structure and verification questions; it does not write the student's deliverable or submission copy.
For each substantial project, record the title and dates, exact student role, question, coursework anchor, new contribution, methods and tools, student-authored code or work, data provenance, results, limitations, iterations, verification, report/demo/repository, and mentor support. One project may create several artifacts; those artifacts are not automatically separate activities.
This is CIT's teaching and documentation standard. It does not replace a school, university, or competition's current official submission rules.
CIT does not assume a project count, duration, or result formula shared by every university, school, or competition. Check the current official route first, then document work so the student can explain the exact role and verify the result truthfully.
| Evidence item | Document | Verify | Reporting rule |
|---|---|---|---|
| Question and new contribution | Summary and log | Sources and requirements | Separate from assessed work |
| Student-authored code, demo, or report | Artifact | Version and authorship | Check privacy and permissions |
| Student role and mentor support | Role record | School, team, mentor | Do not overstate |
| Data, methods, results, and limitations | Reproducibility | Baselines and tests | Include failures |
| Official result or verification | Link evidence | Official record | Check route before use |
| Projects and artifacts | No fixed count | Ownership and continuity | Do not split one activity artificially |
AI projects are not limited to students aiming for a CS major. Students interested in life sciences, humanities, social sciences, business, or the arts can apply AI tools to a field-specific question and keep a verifiable record of the learning process and results. The examples below do not promise an admissions effect or permission to submit.
| Target major/field | AI-fusion project direction | Key tools & technologies |
|---|---|---|
| Life sciences/medicine (Pre-med) | Medical image classification, drug-molecule property prediction, public-health data visualization | Google Teachable Machine, Python (pandas), Kaggle datasets |
| Humanities/social sciences | Sentiment analysis of historical texts, social-media opinion analysis, AI ethics policy research reports | Hugging Face sentiment classification, ChatGPT API, public data portals |
| Business/economics | Automated consumer-review classification, financial-data prediction models, startup pitch-deck automation | Scikit-learn, Tableau, Google Colab (no-code flow) |
| Arts/design | Generative-AI art projects, music-emotion classification, digital-media accessibility tools | Stable Diffusion, Magenta (Google), p5.js + ML5.js |
| Environment/earth sciences | Satellite-image-based deforestation detection, climate-data visualization, carbon-emissions prediction | Google Earth Engine, NASA open data, Python visualization |
CIT matches mentors across the fields above. Students may begin without programming experience, then learn the coding required for the chosen project. They must understand and explain every tool, AI output, line of code, and analysis used in the final work. See the full AI program curriculum →
CIT offers onsite sessions in Apgujeong and online 1:1 or small-group (1:n) sessions. Students elsewhere in Korea or overseas can ask about online participation based on time zone, internet access, subject area, and project stage.
CIT helps map the student's interests, intended major, prior learning, available time, and assessed-work boundary. The student chooses the independent question and remains responsible for every project decision.
Students carry out a real project: implementing an AI model, building an app, analyzing data, and more. Alongside technical mentoring, the development process is documented systematically, and the trial-and-error and improvement process is treated as an important part of the portfolio.
Students write their own project report, GitHub README, and presentation materials. CIT teaches structure and gives permitted feedback on clarity, reproducibility, and whether the student's exact role, methods, results, limitations, and support are disclosed.
We organize the project log, code, data provenance, demo, report, and verification. If an admissions route permits the material, students describe their exact role, dates, outcome, and mentor support truthfully.
U.S. and U.K. applications: Activities, additional information, interviews, and external-link options vary by institution and route. If the current route permits a project description, the student writes it and reports the exact role, dates, outcome, limitations, and mentor support truthfully. See the full EC strategy →
Korean university routes: Whether a portfolio, outside competition, award, or repository may be submitted (and whether school verification is required) depends on the university, admission year, and route. The current official guide takes priority.
School activity: A project may become a school-approved club, service, or research presentation when appropriate. Records must reflect the student's real role and verified outcome.
There is no required project count. Start with one substantial project the student owns and can explain, then document the question, role, data, methods, results, limitations, and verification. The study guides below are learning resources; assessed coursework is not copied into a new project.
A portfolio records a problem the student found, the solution process, a working result, the exact student role, data provenance, iterations, limitations, verification, and mentor support. There is no required project count, and multiple artifacts from one project are not automatically separate activities.
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. CIT offers 1:1 mentoring across life sciences, humanities, business, and the arts.
Record the project title and dates, the student's exact role, question, coursework anchor, new contribution, methods and tools, student-authored code or work, data provenance, results, limitations, iterations, verification, artifacts, and mentor support. This is CIT's documentation standard; check the current official rules for the relevant school, university, competition, or route before submission.
CIT does not assume a project count or fixed duration shared by every route. One sustained project may produce a report, poster, demo, repository, and presentation, but those artifacts are not automatically separate activities. Follow the current official route for any submission limit.
Timing depends on the student's prerequisites, current coursework, examination and assignment deadlines, and intended output. Younger students can build foundations and explore; older students should extend one strong existing area narrowly. Academic performance and required schoolwork come first.
Yes. A competition result is not required to begin CIT's project-evidence process. Record the student's real role, process, and result in independent or team work, and submit the material externally only when the applicable route permits it.
Student-owned coding and AI work may provide evidence of interests, technical skill, and initiative. GitHub, apps, and competition records are not universal requirements. If a route permits the material, describe the student's exact role, dates, outcome, limitations, and mentor support truthfully.
Yes, when GitHub suits the project. We teach profile setup, READMEs, version history, and documentation. GitHub is an optional learning and development record, not a universal admissions requirement or automatic proof of technical ability. Check privacy and external-link rules before sharing it.
Timing depends on the state of the existing work, code and data permissions, new validation required, and the academic calendar. We plan in relative weeks after diagnosis and reduce or pause work around mocks, examinations, and required coursework.
Yes. CIT offers onsite sessions in Apgujeong and online 1:1 or small-group (1:n) sessions. Students elsewhere in Korea or overseas can ask about remote lessons based on time zone, internet access, subject area, and project stage. See the online class guide for the current delivery scope.
Not sure how to get started on a portfolio? In a free consultation, we'll walk you through a portfolio strategy tailored to the student's current level and goals.
Official references (reviewed August 29, 2026)
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.