---
id: wiki-2026-0508-corporate-lms
title: Corporate LMS Training
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [LMS, learning management system, corporate training, talent analytics, adaptive learning, micro-learning]
duplicate_of: none
source_trust_level: B
confidence_score: 0.83
verification_status: applied
tags: [hr, lms, training, corporate-learning, talent-analytics, compliance, ai-personalization]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: HR / SaaS
  framework: Coursera Business / Degreed / Cornerstone / Workday
---

# Corporate LMS Training

## 매 한 줄
> **"매 organization 의 learning 의 platform"**. 매 onboarding + 매 compliance + 매 skill development. 매 modern: 매 micro-learning + 매 gamification + 매 AI-driven adaptive + 매 LXP (Learning Experience Platform). 매 LLM 의 personalized tutor 의 새 era.

## 매 핵심

### 매 3 value
1. **Standardization**: 매 SOP + 매 culture.
2. **Compliance**: 매 법적 / 매 security 의 mandatory.
3. **Talent analytics**: 매 skill inventory.

### 매 modern feature
- **Micro-learning**: 매 5-10 min module.
- **Gamification**: 매 badge, 매 leaderboard.
- **Adaptive path**: 매 ML 의 personalize.
- **Social learning**: 매 peer, 매 mentor.
- **Mobile-first**.
- **Integration**: 매 HRIS, 매 calendar.

### 매 platform tier

#### LMS (traditional)
- Cornerstone.
- SAP SuccessFactors Learning.
- Workday Learning.
- Moodle (open).

#### LXP (Learning Experience Platform)
- Degreed.
- 360Learning.
- EdCast.
- 매 employee-driven (매 self-curate).

#### Content marketplace
- Coursera Business.
- LinkedIn Learning.
- Udemy Business.
- Pluralsight.

#### Modern AI-native
- 매 personalized GPT tutor.
- 매 internal RAG 의 company knowledge.

### 매 응용
1. **Onboarding**: 매 new hire.
2. **Compliance**: GDPR, SOC 2, OSHA 매 annual.
3. **Skill upskill**: 매 emerging tech.
4. **Leadership**: 매 management track.
5. **Sales enablement**.
6. **Tech certification**.

### 매 modern AI 의 응용
- 매 personalized learning path.
- 매 LLM-based Q&A 의 internal docs (RAG).
- 매 adaptive difficulty.
- 매 skill gap analysis.
- 매 content auto-generation (slide, quiz).
- 매 video transcript + summary.

### 매 measurement (Kirkpatrick model)
1. **Reaction**: 매 satisfaction.
2. **Learning**: 매 knowledge gain.
3. **Behavior**: 매 on-job 의 application.
4. **Results**: 매 business outcome.

→ 매 가장 어려움: 매 Level 3-4.

### 매 anti-pattern
- 매 click-through 만 의 compliance.
- 매 long video 의 dump.
- 매 no application opportunity.
- 매 metric (completion) 의 obsession.

## 💻 패턴 (응용 — modern LMS design)

### Personalized learning path (LLM-driven)
```python
def generate_learning_path(employee, goal_role):
    current_skills = inventory_skills(employee)
    target_skills = role_requirements(goal_role)
    gap = target_skills - current_skills
    
    prompt = f"""Design a 12-week learning path.

Current skills: {list(current_skills)}
Target role: {goal_role}
Skill gaps: {list(gap)}
Available time: 5 hours/week
Learning style: {employee.preference}

Output: weekly modules with mix of courses, projects, mentorship.
Format JSON."""
    return llm.generate_json(prompt)
```

### Spaced repetition for compliance
```python
class ComplianceTracker:
    def __init__(self):
        self.modules = {}  # 매 user → 매 module → 매 last_pass + interval
    
    def schedule_review(self, user_id, module_id, score):
        prev = self.modules.get((user_id, module_id), {'interval': 7, 'ease': 2.5})
        if score > 0.8:
            interval = int(prev['interval'] * prev['ease'])
        else:
            interval = 7  # 매 reset
        next_review = datetime.now() + timedelta(days=interval)
        self.modules[(user_id, module_id)] = {
            'next': next_review,
            'interval': interval,
            'last_score': score,
        }
```

### Internal RAG tutor
```python
def employee_tutor(employee, question):
    # 매 access control: 매 employee 의 role 의 permitted docs 만
    scope = get_accessible_docs(employee.role)
    relevant = vectordb.similarity_search(question, k=5, filter=scope)
    
    return llm.generate(f"""You are an internal tutor for {employee.company}.
Use only the provided docs. Cite sources.

Docs:
{relevant}

Question: {question}""")
```

### Skill gap analysis
```python
def skill_gap_analysis(team, target_skills):
    inventory = {member: inventory_skills(member) for member in team}
    
    gaps = {}
    for skill in target_skills:
        proficient = sum(1 for m in team if skill in inventory[m] and inventory[m][skill] >= 3)
        gaps[skill] = {
            'proficient_count': proficient,
            'team_size': len(team),
            'coverage': proficient / len(team),
            'priority': 'high' if proficient < len(team) * 0.3 else 'medium',
        }
    return gaps
```

### Gamification (responsibly)
```python
class LearningGameification:
    """매 Cognitive Evaluation Theory 의 careful 적용."""
    
    def award(self, user, action):
        # 매 ❌ Avoid: 매 cash for fun (over-justification)
        # 매 ✅ OK: 매 verbal + 매 unexpected + 매 mastery
        
        if action == 'completed_module':
            return {
                'badge': f'Mastered {action.module}',
                'public_visibility': user.opt_in,  # 매 opt-in social
                'unlocks': next_unlocked_module(user),
            }
        if action == 'helped_peer':
            return {'badge': 'Mentor', 'visible_to_manager': True}
```

### Manager dashboard (skill inventory)
```python
def team_skill_dashboard(team):
    return {
        'team_size': len(team),
        'avg_certification_count': mean(len(m.certs) for m in team),
        'skill_coverage': skill_gap_analysis(team, target_skills),
        'compliance_status': {
            'gdpr': sum(1 for m in team if m.compliance.gdpr_current) / len(team),
            'security': sum(1 for m in team if m.compliance.security_current) / len(team),
        },
        'learning_velocity': hours_learned_last_quarter(team) / len(team),
    }
```

### A/B test (effectiveness)
```python
def ab_test_module(module_v1, module_v2, employees):
    # 매 random assign
    group_a = [e for e in employees if hash(e.id) % 2 == 0]
    group_b = [e for e in employees if hash(e.id) % 2 == 1]
    
    # 매 deliver
    deliver(module_v1, group_a)
    deliver(module_v2, group_b)
    
    # 매 measure (Kirkpatrick L3 — 매 behavior change)
    return {
        'v1_application_rate': measure_on_job(group_a, after_days=30),
        'v2_application_rate': measure_on_job(group_b, after_days=30),
    }
```

### Adaptive difficulty
```python
def adaptive_quiz(user, topic):
    """매 Item Response Theory 기반."""
    estimated_ability = user.skill_level[topic]
    
    next_question = pick_question_with_difficulty_near(estimated_ability + 0.5)  # 매 just-beyond
    
    score = present_and_grade(next_question)
    update_ability(user, topic, score)
    return next_question
```

## 매 결정 기준
| 조직 size | Approach |
|---|---|
| Startup (<50) | LinkedIn Learning + 매 informal |
| SMB (50-500) | Cornerstone / Degreed |
| Enterprise (1000+) | SAP SuccessFactors / Workday |
| Tech-heavy | Pluralsight + 매 internal docs |
| Compliance-heavy | Compliance-focused LMS (NAVEX) |
| Modern + AI | Custom RAG + 매 platform |

**기본값**: LXP + 매 internal RAG tutor + 매 adaptive path + 매 compliance tracker.

## 🔗 Graph
- 변형: [[LMS]] · [[Adaptive-Learning]] · [[Microlearning]]
- 응용: [[Gamification]] · [[Talent-Analytics]]
- Adjacent: [[Cognitive-Evaluation-Theory]] · [[Deliberate-Practice]] · [[Asset-Specific-Knowledge]] · [[Codebase_Onboarding_Guide]] · [[Cognitive Reserve Theory]]

## 🤖 LLM 활용
**언제**: 매 corporate L&D strategy. 매 onboarding design. 매 skill development plan. 매 compliance system.
**언제 X**: 매 individual self-learning (different paradigm). 매 specific HR law (lawyer).

## ❌ 안티패턴
- **Click-through compliance**: 매 actual learning X.
- **Long video dump**: 매 retention X.
- **Completion metric obsession**: 매 game.
- **No application**: 매 Kirkpatrick L3 의 fail.
- **Generic content**: 매 personalization 의 lose.
- **Cash reward (over-justify)**: 매 intrinsic 의 kill.

## 🧪 검증 / 중복
- Verified (Kirkpatrick model, ATD research, Bersin reports).
- 신뢰도 B.
- Related: [[Cognitive-Evaluation-Theory]] · [[Deliberate-Practice]] · [[Asset-Specific-Knowledge]] · [[Codebase_Onboarding_Guide]] · [[Cognitive Reserve Theory]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — modern feature + 매 personalized path / spaced rep / internal RAG / gamify code |
