Question and baseline
Choose a concrete question from a subject or everyday interest, define a success criterion, and build a simple non-AI method before comparing AI.
AI literacy means understanding how AI works, critically evaluating its outputs, and preserving human judgment and responsibility while using it. CIT places students by explainable readiness and school rules rather than one fixed age, with one-to-one and small-group lessons in Apgujeong or online across Korea and overseas time zones.
한국어 대표 경로: 국제학교 AI 교육·EC 포트폴리오
Priority 1 · International school: protect AP, IB, and IGCSE academics and the school's AI-use rules first, then extend a separate student-owned project. International-school AI education & EC portfolio →
Priority 2 · Science or gifted-school preparation: do not assume a common AI portfolio submission; check the official rules for the school, route, and year first. Science and gifted-school AI portfolio →
Projects, school records, grades, awards, and admissions outcomes are not guaranteed. | Published March 12, 2026 · Official sources reviewed August 30, 2026
The OECD and European Commission's 2026 primary and secondary framework defines AI literacy as the knowledge, skills, and attitudes needed to understand how AI systems work, critically evaluate outputs, and use AI ethically and creatively. It includes opportunities and risks, data, limitations, and human judgment and responsibility—not merely tool operation.
UNESCO's 2024 student framework organizes 12 competencies across four dimensions—human-centred mindset, ethics of AI, AI techniques and applications, and AI system design—and three progression levels: Understand, Apply, and Create. CIT uses these international materials as curriculum references, not as a Korean school grading standard or an admissions submission rule.
Boundary: these frameworks are common references. They do not set one required starting age or automatically establish grade improvement, portfolio permission, or an admissions advantage.
These four stages are CIT's readiness-based application of the official frameworks, not a government credential or admissions rubric. A student may begin at a different stage in each area.
Distinguish everyday AI from ordinary programs and explain how inputs, data, and outputs relate. Placement starts with understanding and the ability to follow school and household rules, not a fixed grade.
Compare AI outputs only where current tool terms and school and household rules permit. Do not enter personal data; use independent sources or tests to check factuality, errors, and bias, and record the human judgment.
Use small datasets with clear provenance and permission to study collection, labeling, and bias. Compare a simple non-AI baseline with a classifier and explain errors and limitations.
Record a student question, code, data, experiments, results, errors, limitations, and the scope of mentor or AI support. This is learning evidence and is used only in a format a current school, competition, or application permits.
Recommendation, search, translation, and generative tools influence what information appears and which options receive priority. Students need to distinguish AI output from verified fact and judge data provenance, bias, privacy, copyright, errors, and human responsibility.
Korean Ministry of Education Notice 2022-33 strengthens digital foundational literacy and Informatics education through a staged rollout. In 2026 the revised curriculum applies to elementary grades 1-6, middle-school grades 1-2, and high-school grades 1-2; middle- and high-school grade 3 follow in 2027. That schedule does not mean CIT lessons guarantee grades or admissions outcomes.
A useful project shows more than tool use. It leaves a reproducible record of what the student asked, compared, got wrong, revised, and received help with.
Choose a concrete question from a subject or everyday interest, define a success criterion, and build a simple non-AI method before comparing AI.
Record data permission and provenance, code and experiment versions, errors, bias, limitations, and the exact roles of the student, team, mentor, and AI.
Teacher rules govern school assignments. An independent project is not a universal admissions submission and is used only in the specified format permitted by the school, route, and year.
CIT supports student explanation, code review, validation, and documentation in Apgujeong and through online lessons across Korea and overseas. School records, portfolio permission, grades, awards, and admissions outcomes are not guaranteed.
Official sources checked August 30, 2026. School policies, tool terms, and admissions rules can change; verify the latest originals before lessons and applications.
The UNESCO and OECD/European Commission frameworks do not set one universal starting age. CIT first checks whether a student can distinguish everyday AI, explain the relationship between inputs and outputs and a result's limitations, and follow school and household tool-use rules. A student can begin with no-code understanding activities, then expand into application and creation when ready.
Yes. The understanding stage can begin without code by separating AI output from verified fact and judging provenance, bias, privacy, and human responsibility. Python and mathematics become important tools when a student analyzes data, builds a model, and evaluates it in the application and creation stages.
A generative AI tool is used as an example only when current tool terms and school and household rules permit it. Students do not enter personal data, record their own thinking first, check generated answers against independent sources or tests, and disclose errors, bias, and the scope of assistance. Using a specific tool is not the course goal.
Korea's 2022 revised national curriculum strengthens digital foundational literacy and Informatics education through a staged rollout. In 2026 it applies to elementary grades 1-6, middle-school grades 1-2, and high-school grades 1-2. CIT supports conceptual understanding and verification practice but does not guarantee grades; the student's actual school curriculum and assessment rules come first.
AI literacy includes the knowledge, skills, and attitudes needed to understand how AI works, critically evaluate outputs, and use AI ethically and creatively. AI education may also include data analysis, programming, model implementation, and system design. The two overlap and expand according to readiness rather than forming one rigid sequence.
Depending on readiness and the student's question, the path may expand into Python, data analysis, baseline comparison, machine learning, and a student-owned AI project. The record includes the question, data provenance, code, validation, errors, limitations, exact student role, and the scope of mentor or AI assistance. The student must explain every choice.
Completing a project does not automatically make it an admissions submission or guarantee an evaluation advantage. International-school students protect AP, IB, and IGCSE academics and follow school AI rules first. Science and gifted-school applicants check the official rules for the specific school, route, and year first. Keep an independent project as learning evidence and use it only in a format the current application permits.
A free consultation and placement check identifies what the student can explain and verify now, then aligns the starting point with school rules, subject interests, and project goals.