# WIA-EDU-005: Educational AI Standard
## Specification Version 1.0

**Status:** Approved  
**Date:** 2025-01-15  
**Category:** Education (EDU)  
**Emoji:** 🤖

---

## 1. Abstract

The WIA-EDU-005 Educational AI Standard defines interfaces, protocols, and best practices for implementing artificial intelligence systems in educational contexts. This standard covers AI tutoring systems, automated grading, personalized learning, content generation, and learning analytics.

**弘益人間 (홍익인간)** - This standard embodies the principle of broadly benefiting humanity through accessible, intelligent education technology.

---

## 2. Scope

This standard applies to:

- K-12 education systems
- Higher education institutions
- Corporate training platforms
- Online learning platforms (MOOCs)
- Educational technology vendors
- Learning management systems (LMS)

---

## 3. Definitions

### 3.1 Core Terms

- **AI Tutor**: An intelligent system providing personalized instruction and feedback
- **Automated Grading**: AI-powered assessment and evaluation of student work
- **Personalized Learning**: Customized educational experiences based on individual learner profiles
- **Learning Analytics**: Data-driven insights about learning processes and outcomes
- **Student Model**: Dynamic representation of learner knowledge, skills, and characteristics

---

## 4. AI Tutoring System Requirements

### 4.1 Student Modeling

```typescript
interface StudentProfile {
  studentId: string;
  knowledgeState: KnowledgeMap;
  learningStyle: 'visual' | 'auditory' | 'kinesthetic' | 'reading';
  pace: 'fast' | 'medium' | 'slow';
  masteryLevels: Record<string, number>; // topic -> mastery (0-1)
  engagementMetrics: EngagementData;
}

interface KnowledgeMap {
  concepts: ConceptNode[];
  relationships: ConceptRelationship[];
}
```

### 4.2 Dialogue Management

The AI tutor MUST support:
- Multi-turn conversations with context retention
- Natural language understanding and generation
- Socratic questioning strategies
- Adaptive hint generation
- Positive reinforcement

### 4.3 Response Time

- Text responses: < 2 seconds
- Complex problem solving: < 5 seconds
- Content generation: < 10 seconds

---

## 5. Automated Grading

### 5.1 Supported Assignment Types

1. **Multiple Choice**: Instant grading with 100% accuracy
2. **Mathematical Problems**: Symbolic and numerical validation
3. **Code Assignments**: Test case execution and style analysis
4. **Essays**: NLP-based evaluation with rubric alignment

### 5.2 Grading API

```typescript
interface GradingRequest {
  assignmentId: string;
  studentId: string;
  submissionType: 'essay' | 'code' | 'math' | 'multiple-choice';
  content: string;
  rubric?: Rubric;
}

interface GradingResponse {
  score: number;
  maxScore: number;
  feedback: Feedback[];
  rubricScores?: Record<string, number>;
  gradedAt: Date;
}
```

### 5.3 Feedback Quality

Feedback MUST be:
- Specific and actionable
- Explanatory (why points awarded/deducted)
- Encouraging and constructive
- Aligned with learning objectives

---

## 6. Personalization Engine

### 6.1 Adaptation Dimensions

1. Content difficulty
2. Learning pace
3. Presentation format (visual, textual, interactive)
4. Support level (hints, scaffolding)
5. Learning path sequence

### 6.2 Recommendation API

```typescript
interface RecommendationRequest {
  studentId: string;
  currentTopic?: string;
  context: LearningContext;
}

interface RecommendationResponse {
  nextTopics: Topic[];
  resources: LearningResource[];
  estimatedTime: number; // minutes
  difficulty: number; // 0-1
}
```

---

## 7. Content Generation

### 7.1 Generation Types

- Practice problems and exercises
- Quizzes and assessments
- Explanations and tutorials
- Worked examples
- Interactive simulations

### 7.2 Quality Requirements

Generated content MUST:
- Be factually accurate
- Match specified difficulty level
- Align with curriculum standards
- Be free from bias
- Include proper citations (if applicable)

---

## 8. Learning Analytics

### 8.1 Data Collection

Collect (with consent):
- Performance data (scores, completion rates)
- Behavioral data (time on task, clicks)
- Engagement data (attention, persistence)
- Affective data (frustration, confidence)

### 8.2 Privacy Requirements

- FERPA compliance (US)
- COPPA compliance for children under 13
- GDPR compliance (EU)
- Data minimization principle
- Anonymization where possible
- Encryption at rest and in transit

### 8.3 Analytics API

```typescript
interface AnalyticsQuery {
  studentId?: string;
  classId?: string;
  timeRange: DateRange;
  metrics: Metric[];
}

interface AnalyticsResponse {
  summary: MetricSummary;
  trends: TrendData[];
  predictions: Prediction[];
  recommendations: Intervention[];
}
```

---

## 9. Integration Requirements

### 9.1 LMS Integration

MUST support:
- LTI 1.3 (Learning Tools Interoperability)
- OAuth 2.0 / SAML for SSO
- Grade passback
- Roster sync

### 9.2 API Standards

- REST API with JSON payloads
- OpenAPI 3.0 specification
- Rate limiting: 1000 requests/hour per user
- Authentication: API keys + OAuth 2.0

---

## 10. Ethical Requirements

### 10.1 Algorithmic Fairness

- Regular bias testing across demographic groups
- Diverse training data
- Fairness metrics reporting
- Mitigation strategies for identified bias

### 10.2 Transparency

- Disclosure of AI use to students and parents
- Explainable AI decisions
- Audit trail for grading decisions

### 10.3 Human Oversight

- Teacher review for final grades (recommended)
- Student appeal process
- Human-in-the-loop for high-stakes decisions

---

## 11. Performance Metrics

### 11.1 Learning Outcomes

- Pre-test to post-test gains
- Time to mastery
- Knowledge retention
- Transfer to new contexts

### 11.2 System Metrics

- Uptime: 99.9%
- Response time: <2s for 95th percentile
- Accuracy: >95% for automated grading
- User satisfaction: >4.0/5.0

---

## 12. Security Requirements

- TLS 1.3 for data in transit
- AES-256 encryption for data at rest
- Multi-factor authentication for admin access
- Regular security audits
- Penetration testing annually

---

## 13. Accessibility

MUST comply with:
- WCAG 2.1 Level AA
- Section 508 (US)
- Support for screen readers
- Keyboard navigation
- Adjustable font sizes and contrast

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## 14. Compliance

This standard is compatible with:
- IEEE Learning Technology Standards
- IMS Global Learning Consortium Standards
- ISO/IEC 40180 (Quality models for AI systems)

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## 15. Version History

- **v1.0** (2025-01-15): Initial release

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**弘益人間 (홍익인간) · Benefit All Humanity**