Course Overview
- Course Name: 인공지능 생활 탐구 (Exploring Daily Life with AI)
- School: 대정여자고등학교 (Daejeong Girls’ High School, Autonomous Public School)
- Course Type: Elective (2022 Revised Curriculum)
- Credits/Hours: 2 credits / 2 hours per week (50 min × 2 periods, 10 min break)
- Schedule: Every Thursday, 09:20–11:20
- Target: 2nd year students (elective enrollment), approx. 24 students
- Period: 2026 Spring Semester (16 weeks, 13 instructional weeks + exam/event weeks)
- Textbook: 인공지능 생활 탐구 (충청북도교육청, 1학기)
Teaching Team
| Role | Name | Responsibility |
|---|
| Instructor | Young Joon Lee (이영준) | Language/ChatGPT domain, curriculum coordination |
| Instructor | Yongjun Chang (장용준) | Vision/image AI domain |
| School Coordinator | Yun-Su Kim (김윤수) | School administration, student management, materials |
Instructors alternate biweekly (Week 1 joint orientation; flexible adjustment for travel/scheduling).
Learning Objectives
- Understand the basic concepts and principles of AI, and identify problems in daily life that AI can help solve.
- Compare and use AI tools and platforms, and implement simple algorithms through Python coding.
- Apply AI inquiry tools to real-world problems in home life, school, community, and ESG contexts.
- Develop responsible, critical attitudes toward AI use as digital citizens.
Prerequisites
None. General curiosity about technology and daily life is sufficient. No prior programming experience required.
Course Schedule
Unit 1: Understanding AI in Daily Life (Weeks 1–4)
| Week | Date | Topic | Standards |
|---|
| 1 | 3/6 | AI basic concepts and principles (joint orientation) | 01-01, 01-02 |
| 2 | 3/13 | AI cases in daily life and their impact | 01-03, 01-04 |
| 3 | 3/20 | AI inquiry procedure and process | 01-05 |
| 4 | 3/27 | AI and data science (preprocessing, ML pipeline) | 01-06 |
| Week | Date | Topic | Standards |
|---|
| 5 | 4/3 | AI platforms — comparison and use | 02-01, 02-02 |
| 6 | 4/10 | AI programming languages (Python focus) | 02-03, 02-04 |
| 7 | 4/17 | Algorithm design and coding ① | 02-04 |
| 8 | 4/24 | Algorithm design and coding ② | 02-04 |
| — | 4/28–5/1 | Midterm exam period | — |
| 9 | 5/8 | AI technology tools (Teachable Machine, Colab) | 02-05, 02-06 |
Unit 3: Real-World Applications (Weeks 10–13)
| Week | Date | Topic | Standards |
|---|
| — | 5/15 | School sports day | — |
| 10 | 5/22 | AI in home life | 03-01, 03-02 |
| 11 | 5/29 | AI in school life | 03-03, 03-04 |
| 12 | 6/5 | AI in community/local society | 03-05, 03-06 |
| 13 | 6/12 | AI and ESG (environment, social, governance) | 03-07, 03-08 |
Final Project (Weeks 14–16)
| Period | Content |
|---|
| 6/15–6/26 | Independent inquiry project — topic selection, research, solution design |
| 6/29–7/10 | Project report completion and final presentation |
Students independently select a problem from home, school, community, or ESG domains and apply AI tools to propose and document a solution. Evaluated on process rigor, not just output.
Assessment
| Component | Weight | Method |
|---|
| Presentation/Discussion | ~30% | Unit 1 presentations and peer discussion |
| Lab Observations | ~30% | In-class coding and tool-use assessments (Units 2–3) |
| Inquiry Project Report | ~40% | Final written report + presentation (Weeks 14–16) |
Assessment focuses on process over results. Change tracking (Git/Colab commit history) may be used to evaluate iterative work. Rubrics emphasize the quality of reasoning, tool selection rationale, and ethical reflection.
Textbook & References
- Primary: 인공지능 생활 탐구 (충청북도교육청, 2022 개정 교육과정)
- Supplementary: Weekly instructor-prepared handouts (학생자료/) aligned to each session
- Online: OpenAI documentation, Anthropic Claude docs, Google ML Crash Course