Antigravity Agent
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10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
200 lines
6.7 KiB
Markdown
200 lines
6.7 KiB
Markdown
---
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id: wiki-2026-0508-edtech-industry-trends
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title: Edtech Industry Trends
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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: [EdTech Trends, Education Technology, Learning Tech]
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duplicate_of: none
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source_trust_level: B
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confidence_score: 0.8
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verification_status: applied
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tags: [edtech, education, ai-tutor, lms, trends]
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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: TypeScript
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framework: Next.js
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---
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# Edtech Industry Trends
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## 매 한 줄
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> **"매 AI tutor 의 mass adoption + skill-based credentialing 의 rise"**. 2020 COVID 의 remote-learning 폭발 이후, 2023 GPT-4 의 ChatGPT 의 학습 의 disrupt — 2026 는 personalized AI tutor (Khanmigo, Duolingo Max), micro-credential (Coursera, Open Badges), 그리고 LXP (Learning Experience Platform) 의 LMS 의 대체 의 dominate trend.
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## 매 핵심
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### 매 2026 핵심 trend
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- **AI tutor 의 ubiquity**: Khanmigo, Duolingo Max, ChatGPT for Education.
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- **Adaptive learning**: knowledge tracing (DKT, BKT), spaced-repetition.
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- **Micro-credential**: stackable certificate, Open Badges 3.0, blockchain anchored.
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- **VR/AR**: Meta Quest for Education, immersive lab.
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- **Skills-based hiring**: degree-optional, portfolio + assessment.
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- **Decline of MOOC giants**: Coursera/edX 의 plateau, niche bootcamp 의 rise.
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### 매 Tech stack
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- **Frontend**: Next.js, React Native, Unity (immersive).
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- **AI**: GPT-5, Claude Opus 4.7, Gemini 2.5, fine-tuned tutor model.
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- **Backend**: PostgreSQL + pgvector, Redis, Kafka.
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- **Standard**: LTI 1.3, xAPI/cmi5, Open Badges, IMS Caliper.
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### 매 응용
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1. K-12 의 personalized math tutor (Khan Academy).
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2. Higher-ed 의 AI TA (Georgia Tech Jill Watson 후속).
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3. Corporate L&D 의 skill graph (Degreed, Cornerstone).
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4. Language (Duolingo, Speak) 의 conversational AI.
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## 💻 패턴
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### LTI 1.3 의 LMS launch
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```typescript
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import jwt from 'jsonwebtoken';
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export async function ltiLaunch(req, res) {
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const idToken = req.body.id_token;
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const decoded = jwt.verify(idToken, getKey, {
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algorithms: ['RS256'],
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audience: process.env.LTI_CLIENT_ID,
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issuer: process.env.LTI_PLATFORM_ISSUER,
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});
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const user = {
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sub: decoded.sub,
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role: decoded['https://purl.imsglobal.org/spec/lti/claim/roles'],
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contextId: decoded['https://purl.imsglobal.org/spec/lti/claim/context'].id,
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};
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req.session.lti = user;
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res.redirect('/activity');
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}
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```
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### Adaptive item selection (BKT)
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```typescript
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// Bayesian Knowledge Tracing
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function bktUpdate(p_known: number, correct: boolean,
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p_T = 0.1, p_S = 0.1, p_G = 0.2) {
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const p_obs = correct
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? (p_known * (1 - p_S)) / (p_known * (1 - p_S) + (1 - p_known) * p_G)
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: (p_known * p_S) / (p_known * p_S + (1 - p_known) * (1 - p_G));
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return p_obs + (1 - p_obs) * p_T; // 매 mastery prob 의 update
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}
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function nextItem(skillStates, items) {
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// 매 ZPD: mastery 0.4-0.7 의 item 의 prefer
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return items
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.map(i => ({ i, score: Math.abs(skillStates[i.skill] - 0.55) }))
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.sort((a, b) => a.score - b.score)[0].i;
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}
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```
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### AI tutor 의 Socratic prompt
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```typescript
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const tutorSystemPrompt = `You are a Socratic tutor. NEVER give the answer.
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- Ask one guiding question at a time.
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- If student is stuck, decompose the problem.
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- Validate effort, gently correct misconceptions.
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- Use student's prior turn to scaffold.
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- After 3 unsuccessful hints, offer worked example, not answer.
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Subject: ${subject}
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Student grade: ${grade}
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Misconceptions log: ${misconceptions.join(', ')}`;
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const response = await anthropic.messages.create({
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model: 'claude-opus-4-7',
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system: tutorSystemPrompt,
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messages: history,
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max_tokens: 400,
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});
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```
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### xAPI 의 statement emit
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```typescript
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async function emitXAPI(actor, verb, object, result) {
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await fetch(`${LRS}/statements`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'X-Experience-API-Version': '1.0.3',
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'Authorization': `Basic ${LRS_AUTH}`,
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},
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body: JSON.stringify({
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actor: { account: { homePage: APP, name: actor.id } },
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verb: { id: `http://adlnet.gov/expapi/verbs/${verb}`, display: { 'en-US': verb } },
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object: { id: `${APP}/activities/${object.id}` },
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result: { score: { scaled: result.score }, completion: result.completed },
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timestamp: new Date().toISOString(),
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}),
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});
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}
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```
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### Open Badges 3.0 (verifiable credential)
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```json
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{
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"@context": ["https://www.w3.org/ns/credentials/v2",
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"https://purl.imsglobal.org/spec/ob/v3p0/context-3.0.3.json"],
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"type": ["VerifiableCredential", "OpenBadgeCredential"],
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"issuer": {"id": "did:web:acme.edu", "name": "Acme Academy"},
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"issuanceDate": "2026-05-10T12:00:00Z",
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"credentialSubject": {
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"id": "did:example:learner123",
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"type": ["AchievementSubject"],
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"achievement": {
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"id": "https://acme.edu/badges/python-mastery",
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"name": "Python Mastery",
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"criteria": {"narrative": "Complete 5 projects + final exam ≥80%"}
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}
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},
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"proof": {"type": "Ed25519Signature2020", "...": "..."}
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}
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```
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### Knowledge graph 의 skill prerequisite
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```cypher
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MATCH (target:Skill {name: 'Calculus I'})
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-[:REQUIRES*1..]->(pre:Skill)
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WITH collect(DISTINCT pre) AS prereqs, target
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MATCH (learner:User {id: $userId})-[:MASTERED]->(s:Skill)
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WITH prereqs, target, collect(s) AS mastered
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RETURN target,
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[p IN prereqs WHERE NOT p IN mastered] AS gap;
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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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| K-12 math/reading | Adaptive engine + AI tutor (Socratic) |
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| Higher-ed CS | Project-based + auto-grader + AI TA |
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| Corporate L&D | Skill graph + micro-credential + xAPI |
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| Language learning | Conversational AI + spaced repetition |
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| Niche bootcamp | Cohort + mentor + portfolio review |
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**기본값**: AI tutor (Socratic) + adaptive engine + xAPI tracking + Open Badges credential.
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## 🔗 Graph
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- 부모: [[Education Technology]]
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- 변형: [[LMS]]
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- 응용: [[Adaptive Learning]]
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## 🤖 LLM 활용
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**언제**: Socratic tutor, content scaffolding generation, formative feedback.
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**언제 X**: high-stakes summative grading 의 LLM 의 sole arbiter 의 X.
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## ❌ 안티패턴
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- **Engagement-only metric**: time-on-app maximization 의 learning outcome 무관.
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- **AI 의 give answer**: tutor 의 cheating tool 의 변질.
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- **No interoperability**: LTI/xAPI 의 ignore — institution 의 lock-in.
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- **Privacy 무시**: FERPA/COPPA 의 minor 의 consent 의 fail.
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- **Credential inflation**: badge 의 rigor 의 X — recognition 의 erode.
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## 🧪 검증 / 중복
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- Verified (HolonIQ Edtech Funding Report 2025, IMS Global LTI/Caliper specs, Open Badges 3.0).
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- 신뢰도 B (industry trends 의 변동 빠름).
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — LTI/BKT/AI-tutor/xAPI/Open-Badges patterns |
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