docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거
Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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Antigravity Agent
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---
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id: wiki-2026-0508-emotionally-intelligent-tutoring
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title: Emotionally Intelligent Tutoring Systems (EITS)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [EITS, affective tutor, AutoTutor, emotionally aware ITS]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.93
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verification_status: applied
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tags: [edtech, ai-tutor, affective-computing, eits, emotion, learning, pedagogy]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: Tutoring system / LLM
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---
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# Emotionally Intelligent Tutoring Systems (EITS)
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## 매 한 줄
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> **"매 student 의 cognitive + emotional 의 동시 의 sense + respond 의 ITS"**. 매 frustration / boredom / confusion / engagement 의 detect → 매 strategy adjust. 매 famous: AutoTutor (Graesser), Affective AutoTutor. 매 modern: 매 LLM tutor + 매 facial / voice + 매 adaptive prompt.
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## 매 핵심
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### 매 emotion of learning (D'Mello)
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- **Engagement**: 매 best.
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- **Confusion**: 매 productive (zone of proximal).
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- **Frustration**: 매 productive 의 X — 매 detect.
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- **Boredom**: 매 challenge ↑.
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### 매 affect detection
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- **Behavioral**: 매 click, dwell, error.
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- **Facial**: 매 brow furrow.
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- **Posture**: 매 lean.
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- **Speech**: 매 hesitation.
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- **Self-report**: 매 emoji slider.
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### 매 response strategy
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- **Frustration** → 매 hint, scaffold.
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- **Boredom** → 매 challenge ↑, novelty.
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- **Confusion** → 매 dwell, allow.
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- **Engagement** → 매 maintain.
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### 매 응용
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1. **Math tutor**: 매 step-by-step.
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2. **Language**: 매 conversation practice.
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3. **Programming**: 매 debug help.
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4. **Adaptive learning**: 매 LMS.
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5. **Reading**: 매 comprehension.
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## 💻 패턴
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### Affect detection (multimodal)
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```python
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class AffectDetector:
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def __init__(self):
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self.face = FacialAnalyzer()
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self.behavior = BehaviorTracker()
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def detect(self, frame, log):
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return {
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'engagement': 0.6 * self.face.attention(frame) + 0.4 * self.behavior.click_rate(log),
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'frustration': self.face.brow_furrow(frame) + self.behavior.delete_count(log),
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'confusion': self.behavior.idle_time(log),
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'boredom': self.face.yawn_count(frame) + self.behavior.skip_count(log),
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}
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```
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### Strategy selector
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```python
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def select_strategy(affect, mastery):
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if affect['frustration'] > 0.7:
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return 'hint_with_encouragement'
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if affect['boredom'] > 0.6 and mastery > 0.7:
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return 'increase_difficulty'
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if affect['confusion'] > 0.5 and mastery < 0.5:
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return 'review_prerequisite'
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if affect['engagement'] > 0.7:
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return 'maintain_flow'
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return 'check_in'
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```
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### LLM tutor with affect prompt
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```python
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def llm_tutor_response(student_msg, affect, history):
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affect_str = f"Frustration: {affect['frustration']:.1f}, Engagement: {affect['engagement']:.1f}"
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prompt = f"""You are a patient, emotionally-aware tutor.
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Student affect: {affect_str}
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{'IMPORTANT: Student frustrated — validate first, then small step.' if affect['frustration'] > 0.6 else ''}
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{'IMPORTANT: Student bored — pivot to challenge.' if affect['boredom'] > 0.5 else ''}
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History: {history[-3:]}
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Student: {student_msg}
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Response:"""
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return llm.generate(prompt)
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```
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### Productive vs unproductive confusion (D'Mello)
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```python
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def classify_confusion(affect_history, performance):
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duration = affect_history.confusion_duration()
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progress = performance.recent_correct_rate()
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if duration > 60 and progress < 0.3:
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return 'unproductive' # 매 intervene
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return 'productive' # 매 let dwell
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```
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### Mastery-aware (Bayesian Knowledge Tracing)
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```python
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class BKT:
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def __init__(self, p_init=0.1, p_learn=0.3, p_slip=0.1, p_guess=0.2):
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self.p_known = p_init
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self.p_learn, self.p_slip, self.p_guess = p_learn, p_slip, p_guess
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def update(self, correct):
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if correct:
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num = self.p_known * (1 - self.p_slip)
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denom = num + (1 - self.p_known) * self.p_guess
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else:
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num = self.p_known * self.p_slip
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denom = num + (1 - self.p_known) * (1 - self.p_guess)
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self.p_known = num / denom + (1 - num / denom) * self.p_learn
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```
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### Engagement intervention (recovery)
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```python
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def recover_engagement(disengaged_for_seconds):
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if disengaged_for_seconds < 30:
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return None
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if 30 <= disengaged_for_seconds < 120:
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return {'type': 'gentle_check_in', 'msg': 'Still with me?'}
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return {'type': 'pivot', 'msg': "Let's try something different."}
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```
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### Self-report (emoji slider)
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```html
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<div class="affect-checkin">
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How are you feeling?
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<button data-emotion="frustrated">😤</button>
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<button data-emotion="confused">🤔</button>
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<button data-emotion="bored">😴</button>
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<button data-emotion="engaged">🤩</button>
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</div>
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```
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### Empathy response template
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```python
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EMPATHIC_OPENERS = {
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'frustrated': ["This one is tricky — that's a normal feeling.", "Let's slow down a bit."],
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'confused': ["I see what's confusing — let me explain differently.", "Good question."],
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'bored': ["Let me show you why this matters.", "Here's a more interesting twist."],
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}
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def open_response(detected_emotion):
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return random.choice(EMPATHIC_OPENERS.get(detected_emotion, ['']))
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```
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### A/B test (affect-aware vs not)
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```python
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def evaluate_eits(group_a_baseline, group_b_eits):
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return {
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'completion_a': mean(s.completed for s in group_a_baseline),
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'completion_b': mean(s.completed for s in group_b_eits),
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'satisfaction_a': mean(s.rating for s in group_a_baseline),
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'satisfaction_b': mean(s.rating for s in group_b_eits),
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'mastery_a': mean(s.mastery for s in group_a_baseline),
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'mastery_b': mean(s.mastery for s in group_b_eits),
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}
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Webcam OK | Multimodal facial + behavior |
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| No camera | Behavioral + self-report |
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| K-12 | Strong empathy emphasis |
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| Higher-ed | Productive confusion tolerated |
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| Adult learning | Less interruption |
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| Mental health risk | Clinician escalation path |
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**기본값**: 매 multimodal affect + 매 BKT mastery + 매 LLM empathic response + 매 self-report fallback + 매 productive confusion 의 respect.
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## 🔗 Graph
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- 부모: [[Education-Technology]]
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- 변형: [[AutoTutor]] · [[Emotional-AI (Affective Computing)]]
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- 응용: [[Adaptive-Learning]]
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- Adjacent: [[Emotional-AI (Affective Computing)|Empathy-in-AI]] · [[Dynamic Difficulty Adjustment (DDA)]] · [[Corporate-LMS-Training]]
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## 🤖 LLM 활용
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**언제**: 매 tutoring product. 매 K-12. 매 language learning.
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**언제 X**: 매 reference material. 매 assessment-only.
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## ❌ 안티패턴
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- **Surveillance feel**: 매 student 의 creep.
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- **All confusion = bad**: 매 productive 의 ignore.
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- **Static empathy**: 매 personalize X.
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- **Privacy violation**: 매 video 의 cloud send.
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- **Validate-only**: 매 challenge X.
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## 🧪 검증 / 중복
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- Verified (D'Mello & Graesser, AutoTutor, 2014+ EITS literature).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-04-20 | Auto-reinforced |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — affect-aware + 매 detect / strategy / BKT / LLM / A/B code |
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