이 책은 인공지능, 특히 Chat GPT와 같은 대형언어모델(LLM)이 데이터를 학습하는 원리와, 인간의 학습 과정을 비교하여 부모와 교육자들에게 새로운 교육 패러다임을 제시하는 책입니다.

 

LLM의 학습 프로세스는 기본적으로 Data 입력, 반복 학습, 피드백을 통한 개선으로 이루어지며, 이는 사람이 학습하는 과정과 매우 유사합니다. 하지만 가장 중요한 차이점은 감정적 상호작용, 사회적 관계, 그리고 창의성 개발에서 드러납니다.

 

사람이 '기초부터 천천히' 학습하는 것이 왜 중요한지, 단계별 맞춤형 학습 전략이 지능 발달에 어떻게 기여하는지에 대해 구체적이고 실질적인 방법이 담겨있습니다.

[목 차]

머릿글· ···························································· 4

들어가며: 데이터와 아이들·········································· 12

 

제1장

기초적인 데이터 입력 - 기본적인 가치와 규범

아이들의 초기 학습과 가치관 형성···································· 34

데이터 사이언스와 아이들과의 상관관계······························ 39

 

제2장

반복 학습과 강화 - 일관된 교육의 중요성

1. LLM의 반복 학습과 아이들의 반복 학습 필요성····················· 49

2. 일관성 있는 반복의 중요성········································ 49

3. 강화 학습과 Few-Shot Learning·································· 50

4. Augmentation과 다양한 학습 자극······························· 51

5. 반복 학습의 한계와 실수에서 배우는 법···························· 52

6. ReAct Prompting과 비판적 사고의 훈련··························· 53

7. 자기주도 학습과 일관된 Feedback의 중요성························ 54

8. 반복, 강화, 그리고 창의적 학습···································· 54

 

제3장

적절한 Feedback - 아이들에게 정보를 다듬어 주는 과정

1. LLM의 Chain of Thought와 아이들의 사고 과정··················· 58

2. LLMOps와 아이들의 지속적인 학습 발전· ························· 61

3. Data Mart 개선과 학습 데이터의 정리····························· 62

4. Data Cleansing과 학습 오류 수정································· 63

5. CICD와 지속적인 학습 개선······································ 64

6. Feedback Loop와 학습 강화····································· 67

7. Active Learning과 문제 해결 능력 향상··························· 68

8. 지속적인 Feedback과 학습 개선의 힘······························ 69

 

제4장

개인화된 학습 - 각 아이에게 맞춘 교육 전략

1. LLM의 Persona와 아이들의 학습 맞춤화·························· 73

2. Embeddings와 학습 스타일 분석································· 74

3. Transfer Learning과 학습 속도 조절······························ 75

4. Few-Shot Learning과 맞춤형 과제 제공··························· 76

5. GPT의 개인화된 사용과 맞춤형 학습 응용························· 77

6. Fine-Tuning된 AI 서비스와 아이들의 학습 지원···················· 78

7. GPT의 개인화된 사용과 맞춤형 학습 응용·························· 79

8. 맞춤형 학습 전략과 개인화된 교육의 힘···························· 80

 

제5장

실패와 학습 - 잘못된 데이터의 처리와 아이들의 실수

1. LLM이 잘못된 데이터를 처리하는 방법과 교정 메커니즘············· 84

2. 실수를 학습의 기회로 삼기······································· 85

3. 실수에서 배우는 힘을 키우는 법··································· 87

4. LLM과 아이의 차이점: 인간적 감정과 상호작용의 중요성············ 88

5. 주의사항: 인간적인 감정과 연결을 무시하지 말아야 한다············· 90

 

제6장

창의력 개발 - 데이터 이상의 것을 배우기

1. 패턴을 넘어선 사고: LLM의 한계와 아이의 창의적 가능성··········· 96

2. 창의적 사고를 키우는 환경 조성··································· 97

3. 상상력의 힘: 데이터를 넘어선 상상 놀이···························· 99

4. 감정과 창의성: LLM과 아이의 학습 차이·························· 100

5. 실패에서 배우는 창의성: LLM의 오류와 아이의 실수··············· 101

6. 상호작용을 통한 창의성 발달···································· 102

 

제7장

사회적 상호작용 - LLM이 하지 못하는 인간의 능력

1. 감정 인식과 공감: LLM과 인간의 본질적 차이····················· 106

2. 협력과 타협: 인간 사회의 필수 기술······························· 109

3. 비언어적 의사소통: 말이 아닌 신호의 중요성······················· 113

4. 갈등 해결과 관계 구축: 인간만이 할 수 있는 과정·················· 115

5. 사회적 상호작용의 본질과 인간의 능력···························· 117

 

제8장

정보 과부하 - 데이터를 넘어서기

1. 정보 필터링: LLM의 데이터 정리와 아이들의 정보 관리············· 121

2. 정보 과부하 방지: 학습 시간과 정보량의 조절······················ 122

3. 정보 과부하 해결: Data Compression과 핵심 정보 추출············ 123

4. 정보 정리와 시각적 자료 활용: Multi-Modal Learning············· 125

5. 정보 선택과 집중: Active Learning과 정보 선별 능력·············· 127

6. 정보 과부하 속에서 핵심에 집중하기······························ 128

 

제9장

AI Way of Thinking 실전

1. 중요한 수학 시험에서 30점을 맞아온 아이························· 135

2. 매번 반 1등을 하던 아이, 자퇴를 선언하다························· 138

3. 남자친구가 생긴 중2 딸, 성적이 떨어지고 미술을 하겠다고 선언하다·· 142

4. 초등학교 2학년, 글 읽기를 거부하는 아이························· 147

5. 스마트폰에 매달린 아이, 강제로 끊으려니 더 큰 반항··············· 151

6. “꿈은 돈을 많이 버는 것”이라고 말하는 아이······················ 155

7. 왕따를 당하며 “왜 살아야 하는지 모르겠어”라고 말하는 아이······· 159

 

제10장

인지발달 이론과 AI 이론의 만남 - 인간과 기계의 학습 이론의 비교

1. 서론··························································· 166

2. 주요 인지발달 이론 소개········································· 168

3. AI와 인지발달 이론의 공통점···································· 183

4. AI와 인간 인지발달의 차이점···································· 191

5. 미래 전망: 인지발달 연구와 AI 발전의 융합 가능성················· 195

6. 결론··························································· 200

 

제11장

아동 및 청소년 학습 이론과 AI의 학습 기법 비교

1. 서론··························································· 204

2. 학습이란 무엇일까?············································· 206

3. 아동과 청소년이 배우는 방법: 학습 이론 탐구······················ 210

4. AI와 딥러닝의 학습 방식: 기계는 어떻게 배울까?··················· 218

5. 학습 이론과 AI 기법의 흥미로운 비교····························· 228

6. 학습의 미래: 인간과 AI가 함께 배우는 세상······················· 242

 

결론:

아이들이 LLM을 넘어 배우게 하라

1. LLM과 인간의 학습 차이········································ 249

2. 실수와 Feedback: LLM처럼, 그러나 더 나아가···················· 249

3. 창의성과 문제 해결: LLM을 넘어서는 인간의 가능성· ·············· 250

4. 부모의 역할: 지속적인 지도와 성장의 기회 제공···················· 251

5. LLM을 넘어 인간답게 배우게 하라· ······························ 252

마치며· ·························································· 254

 

부록

AI 관련 용어집··················································· 260

아이들과 함께하는 10그래 가지 창의적 Activity 상세 가이드·········· 287