1. You will never be able to detect the use of AI in homework. Full stop. All "detectors" of AI imo don't really work, can be defeated in various ways, and are in principle doomed to fail. You have to assume that any work done outside classroom has used AI.
2. Therefore, the majority of grading has to shift to in-class work (instead of at-home assignments), in settings where teachers can physically monitor students. The students remain motivated to learn how to solve problems without AI because they know they will be evaluated without it in class later.
3. We want students to be able to use AI, it is here to stay and it is extremely powerful, but we also don't want students to be naked in the world without it. Using the calculator as an example of a historically disruptive technology, school teaches you how to do all the basic math & arithmetic so that you can in principle do it by hand, even if calculators are pervasive and greatly speed up work in practical settings. In addition, you understand what it's doing for you, so should it give you a wrong answer (e.g. you mistyped "prompt"), you should be able to notice it, gut check it, verify it in some other way, etc. The verification ability is especially important in the case of AI, which is presently a lot more fallible in a great variety of ways compared to calculators.
4. A lot of the evaluation settings remain at teacher's discretion and involve a creative design space of no tools, cheatsheets, open book, provided AI responses, direct internet/AI access, etc.
TLDR the goal is that the students are proficient in the use of AI, but
can also exist without it, and imo the only way to get there is to flip classes around and move the majority of testing to in class settings.
- '숙제(Homework)'의 종말과 '교실(Classroom)'의 부활
기존 교육에서 성실성의 지표였던 '과제'는 이제 평가 도구로서의 신뢰성을 상실했습니다. 이는 '플립 러닝(Flipped Learning)'과 유사한 구조적 변화를 강제합니다. 지식 습득과 AI 활용 연습은 집에서 하되, 진정한 실력 검증은 교사의 눈 앞에서 아날로그적 방식(또는 통제된 디지털 방식)으로 이루어져야 합니다.
- '검증자(Verifier)'로서의 인간 역량 강화
AI를 잘 쓰는 것보다 중요한 것은 AI가 내놓은 답이 맞는지 틀린지 판별하는 능력(Gut check)입니다. 역설적으로, AI를 잘 활용하기 위해서는 AI 없이도 문제를 풀 수 있는 '기초 근력(Fundamental Skills)'이 필수적입니다. 기초 없이 AI만 쓰는 것은 "세상에 발가벗겨진(naked)" 상태와 같습니다.
- 평가 환경의 다각화 (Spectrum of Assessment)
평가는 단일 방식이 아니라 스펙트럼(No AI <---> Full AI)을 가져야 합니다. 교육자는 이제 'AI 차단'에 골몰할 것이 아니라, 어떤 과제에서 AI를 허용하고 어떤 과제에서 차단할지를 전략적으로 설계하는 '학습 경험 디자이너'가 되어야 합니다.