Using no-code tools such as Scratch and Teachable Machine, students collect data, compare models under two conditions, and explain errors and limitations in their own words. E1 is not direct KOAI preparation or an automatic portfolio course; it builds readiness and a first student-owned learning record for later AI study.
Published: May 16, 2026 | Last updated: August 30, 2026 · Ended KOAI 2026 official record checked
CIT supports Apgujeong in-person lessons and online lessons across Korea or overseas. Each route begins with the current school, division and admissions rules.
Priority route 1
Protect AP, IB and IGCSE academics first, then extend into a student-owned AI project and extracurricular record.
Priority route 2
Check official rules first and record a student-owned question, baseline, experiment, errors and limitations.
Track
AI foundation record
Not direct KOAI preparation
Entry basis
Readiness based
Grades 2-3 common, not fixed
Recommended Hours
About 10 hours
Adjusted by class format · varies 6-14 hours
KOAI Mapping
Not direct prep
Recheck future official rules
By the end of E1, students can explain visually and experientially what AI is and how it learns. They collect data, compare two model conditions, and explain what changed, where the model failed, and what data might be needed next. Korean-English AI vocabulary, computational thinking and data intuition are built alongside that explanation.
E1 is not direct KOAI preparation. The ended 2026 season accepted enrolled students in Grades 7-9 and 10-12; elementary students were not eligible for that season. The value of E1 is a foundation for explaining one's own question, data, experiment and errors, not a promise of future selection. Future eligibility and format must be checked in the new season's official rules.
We check whether the student can read short instructions, use a mouse and keyboard, and explain an activity result in their own words. E1 is for students with no Python experience and requires no separate prior coding. Python begins in the next course, E2.
Below is the standard plan for online or Apgujeong in-person, 1:1 or small-group lessons. Content units may be compressed or extended according to readiness and pace. Core tools: Scratch + ScratchML, Google Teachable Machine, Quick Draw, AutoDraw, AI for Oceans (code.org), Google AI Experiments.
| Week | Topic | Key Deliverable |
|---|---|---|
| 1-2 | What is AI: comparing how humans, animals, and machines learn | Korean-English AI vocabulary cards |
| 3-4 | How computers see pictures (Quick Draw, AutoDraw) | A first data-collection activity |
| 5-6 | How computers hear speech (speech recognition experience) | A voice-command interactive |
| 7-9 | Training a first model with Teachable Machine (image classification) | One classification model |
| 10-11 | Bias and fairness (kid-friendly examples) | Case discussion notes |
| 12-15 | Scratch + ScratchML mini-project | An AI-powered mini-game |
| 16-18 | Data intuition (reading graphs and tables, collecting everyday data) | One data visualization |
| 19-20 | Presentation to family & friends | Presentation video |
※ Weeks are content units; actual time varies by student. Recommended about 10 hours, ranging 6-14 hours.
Students keep Korean-English AI vocabulary cards, a data-collection sheet, a two-condition model comparison, error examples, and bias-and-fairness discussion notes. Public links are used only with guardian consent and where platform age and sharing rules allow; otherwise evidence stays local.
Students create one Scratch AI project and one Teachable Machine demo, then explain their question, data source, comparison result, errors, limitations, and help received. Reproducible explanation matters more than the number of outputs.
E1 outputs are the student's own first AI learning records. Scratch or Teachable Machine results and public links do not by themselves become a KOAI, science/gifted-school, international-school EC, or university submission. Before submitting anything, check the current year, division and school's official rules, allowed format, privacy, copyright and student-authorship requirements.
Student authorship
The student explains the question, data, comparison, revision and their own role
Sources and support
Record data source and consent, mentor or generative-AI help, and limitations
Submission decision
Submit only a format permitted by the current official rules
Official-record boundary · checked August 30, 2026
On KITPA's ended KOAI 2026 official page, the High School first-stage portfolio review was weighted at 40%. The Middle School division did not publish the same 40% portfolio review. This is an ended 2026 High School division record, not a rule for future seasons, other divisions, or school admissions.
E1 is a readiness-based introduction before considering later Python and machine-learning study. See the KOAI Curriculum Hub for the learning sequence, while any competition entry follows the future season's official rules.
Previous Step (Prerequisite)
Readiness check
Reading, basic controls, self-explanation
Current Course
E1. Elementary AI Introduction
No-code AI literacy
Grades 2-3 are a common recommendation, not a fixed starting rule. We first check whether a student can read short instructions, use a mouse and keyboard, and explain their own data and model result in their own words.
No. E1 is a no-code AI literacy and first student-owned learning-record course. The ended KOAI 2026 season accepted enrolled middle-school and high-school students, so elementary E1 was not an entry course for that season. Future eligibility and dates must be checked in that season's official rules.
Yes. E1 is designed for students with no Python experience; they explore AI principles using no-code tools like Scratch and Teachable Machine. Python begins in the next course, E2.
It does not happen automatically. Scratch and Teachable Machine outputs are learning records. The student's own question, data source and consent, comparison criteria, errors, limitations, role, and mentor or AI support must be explainable. Submission is a separate decision only where the current competition or school's official rules allow it.
The ended 2026 High School division assigned 40% to the first-stage portfolio review. The Middle School division did not publish the same 40% portfolio review. That archived rule cannot be generalized to a future season or to science schools, gifted schools, international schools, or university admissions.
Depending on readiness and goals, students may consider E2 (Elementary Python + Data), E3 (Elementary First ML), and later M1. This is a recommended learning sequence, not a promise of KOAI eligibility, selection, or awards; any entry must follow the future season's official rules.
We check reading, basic controls, self-explanation readiness and goals to choose an online or Apgujeong in-person starting point. Awards, selection and admissions outcomes are not guaranteed.