Educational Learning Multi-Agent System
Build an AI-powered personalized learning platform with specialized agents for curriculum design, student assessment, content recommendation, and adaptive tutoring.
Overview
This example demonstrates how to build a multi-agent educational system that can:
- Assess student learning styles and knowledge levels
- Design personalized curriculum paths
- Recommend appropriate learning content
- Provide adaptive tutoring and feedback
- Track learning progress and outcomes
- Generate performance reports for educators
- Support multiple subjects and grade levels
Perfect for: E-learning platforms, educational institutions, tutoring services, and corporate training systems.
How It Works
Workflow Steps
- Student Assessment: Manager agent evaluates student profile and learning needs
- Learning Style Analysis: Identifies optimal learning approaches for the student
- Curriculum Planning: Creates personalized learning path
- Content Delivery: Provides appropriate learning materials
- Interactive Tutoring: Offers real-time assistance and explanations
- Progress Monitoring: Tracks performance and adjusts difficulty
- Feedback Generation: Provides detailed feedback to students and educators
Agent Hierarchy
Install AgentRouter
# Install AgentRouter SDK
!pip install agentrouter -q
# Import required libraries
from agentrouter import ManagerAgent, WorkerAgent, tool
from agentrouter.visualization import PipelineInspector
import asyncio
import nest_asyncio
from typing import Dict, Any, List, Optional
from datetime import datetime, timedelta
import json
import random
nest_asyncio.apply()
Complete Code
# ============================================
# EDUCATIONAL LEARNING MULTI-AGENT SYSTEM
# ============================================
# Configuration
API_KEY = "your-api-key-here" # Replace with your actual API key
# ============================================
# TOOL DEFINITIONS
# ============================================
@tool(
schema={
"type": "function",
"function": {
"name": "assess_student_level",
"description": "Evaluates student knowledge, identifies strengths/weaknesses, and determines learning style",
"parameters": {
"type": "object",
"properties": {
"student_id": {
"type": "string",
"description": "Student identifier"
},
"subject": {
"type": "string",
"enum": ["math", "science", "language", "history", "computer_science"],
"description": "Subject to assess"
},
"assessment_type": {
"type": "string",
"enum": ["diagnostic", "formative", "summative"],
"description": "Type of assessment"
}
},
"required": ["student_id", "subject"]
}
}
}
)
async def assess_student_level(student_id: str, subject: str, assessment_type: str = "diagnostic") -> Dict[str, Any]:
"""Assess student's knowledge level and learning preferences"""
# Simulated assessment results
assessments = {
"math": {
"current_level": "Grade 8",
"proficiency": 75,
"strengths": ["Algebra", "Geometry"],
"weaknesses": ["Trigonometry", "Word Problems"],
"recent_topics": ["Linear Equations", "Quadratic Functions"]
},
"science": {
"current_level": "Grade 9",
"proficiency": 82,
"strengths": ["Biology", "Chemistry"],
"weaknesses": ["Physics", "Lab Reports"],
"recent_topics": ["Cell Biology", "Chemical Reactions"]
},
"language": {
"current_level": "B2 Intermediate",
"proficiency": 68,
"strengths": ["Reading Comprehension", "Vocabulary"],
"weaknesses": ["Grammar", "Writing"],
"recent_topics": ["Essay Writing", "Literature Analysis"]
}
}
assessment = assessments.get(subject, {
"current_level": "Beginner",
"proficiency": 50,
"strengths": [],
"weaknesses": ["Fundamentals"],
"recent_topics": []
})
return {
"student_id": student_id,
"subject": subject,
"assessment_type": assessment_type,
"results": assessment,
"learning_style": {
"primary": "visual",
"secondary": "kinesthetic",
"preferences": ["interactive_content", "videos", "practice_problems"]
},
"recommended_pace": "moderate",
"assessment_date": datetime.now().isoformat()
}
@tool(
schema={
"type": "function",
"function": {
"name": "create_learning_path",
"description": "Generates personalized curriculum with modules, objectives, and timeline based on student needs",
"parameters": {
"type": "object",
"properties": {
"student_id": {
"type": "string",
"description": "Student identifier"
},
"subject": {
"type": "string",
"description": "Subject for learning path"
},
"duration": {
"type": "string",
"description": "Duration of learning path (e.g., '4 weeks', '3 months')"
},
"goal": {
"type": "string",
"description": "Learning goal or target"
}
},
"required": ["student_id", "subject", "goal"]
}
}
}
)
async def create_learning_path(student_id: str, subject: str, goal: str, duration: str = "4 weeks") -> Dict[str, Any]:
"""Create personalized curriculum path"""
# Simulated learning path generation
learning_paths = {
"math": {
"modules": [
{
"week": 1,
"topic": "Algebraic Expressions",
"objectives": ["Simplify expressions", "Solve linear equations"],
"activities": ["Video lessons", "Practice problems", "Quiz"],
"estimated_hours": 5
},
{
"week": 2,
"topic": "Quadratic Equations",
"objectives": ["Factor quadratics", "Use quadratic formula"],
"activities": ["Interactive tutorial", "Problem sets", "Peer review"],
"estimated_hours": 6
},
{
"week": 3,
"topic": "Functions and Graphs",
"objectives": ["Graph functions", "Identify transformations"],
"activities": ["Graphing exercises", "Real-world applications", "Project"],
"estimated_hours": 7
},
{
"week": 4,
"topic": "Review and Assessment",
"objectives": ["Consolidate learning", "Demonstrate mastery"],
"activities": ["Review sessions", "Practice test", "Final assessment"],
"estimated_hours": 5
}
]
}
}
return {
"student_id": student_id,
"subject": subject,
"goal": goal,
"duration": duration,
"learning_path": learning_paths.get(subject, {
"modules": [{"week": 1, "topic": "Introduction", "objectives": ["Basics"], "activities": ["Orientation"], "estimated_hours": 3}]
}),
"total_hours": 23,
"difficulty_progression": "gradual",
"created_at": datetime.now().isoformat(),
"start_date": datetime.now().strftime("%Y-%m-%d"),
"end_date": (datetime.now() + timedelta(weeks=4)).strftime("%Y-%m-%d")
}
@tool(
schema={
"type": "function",
"function": {
"name": "recommend_content",
"description": "Suggests videos, articles, games, and practice materials matched to student profile",
"parameters": {
"type": "object",
"properties": {
"student_id": {
"type": "string",
"description": "Student identifier"
},
"topic": {
"type": "string",
"description": "Learning topic"
},
"content_type": {
"type": "string",
"enum": ["video", "article", "interactive", "practice", "game"],
"description": "Type of content to recommend"
},
"difficulty": {
"type": "string",
"enum": ["beginner", "intermediate", "advanced"],
"description": "Content difficulty level"
}
},
"required": ["student_id", "topic"]
}
}
}
)
async def recommend_content(student_id: str, topic: str, content_type: str = "mixed", difficulty: str = "intermediate") -> Dict[str, Any]:
"""Recommend appropriate learning content"""
content_library = {
"Algebraic Expressions": [
{
"title": "Introduction to Algebra",
"type": "video",
"duration": "15 min",
"difficulty": "beginner",
"url": "content/algebra/intro",
"rating": 4.8
},
{
"title": "Algebra Practice Problems",
"type": "practice",
"questions": 25,
"difficulty": "intermediate",
"url": "content/algebra/practice",
"adaptive": True
},
{
"title": "Algebra Adventure Game",
"type": "game",
"duration": "30 min",
"difficulty": "intermediate",
"url": "content/algebra/game",
"engagement_score": 9.2
}
]
}
recommendations = content_library.get(topic, [
{
"title": f"Introduction to {topic}",
"type": "article",
"duration": "10 min",
"difficulty": difficulty,
"url": f"content/general/{topic.lower().replace(' ', '_')}"
}
])
return {
"student_id": student_id,
"topic": topic,
"recommendations": recommendations,
"personalization_score": random.uniform(0.75, 0.95),
"based_on": ["learning_style", "past_performance", "engagement_history"],
"generated_at": datetime.now().isoformat()
}
@tool(
schema={
"type": "function",
"function": {
"name": "provide_tutoring",
"description": "Delivers step-by-step explanations, hints, and practice problems for student questions",
"parameters": {
"type": "object",
"properties": {
"student_id": {
"type": "string",
"description": "Student identifier"
},
"question": {
"type": "string",
"description": "Student's question or problem"
},
"subject": {
"type": "string",
"description": "Subject area"
},
"hint_level": {
"type": "string",
"enum": ["minimal", "moderate", "detailed"],
"description": "Level of assistance to provide"
}
},
"required": ["student_id", "question"]
}
}
}
)
async def provide_tutoring(student_id: str, question: str, subject: str = "general", hint_level: str = "moderate") -> Dict[str, Any]:
"""Provide tutoring assistance"""
# Simulated tutoring responses
tutoring_responses = {
"solve": {
"understanding": "I see you're working on solving equations. Let's break this down step by step.",
"hint": "Remember to isolate the variable by performing the same operation on both sides.",
"example": "If we have 2x + 5 = 15, we first subtract 5 from both sides: 2x = 10",
"practice": "Try this similar problem: 3x + 7 = 22",
"resources": ["Video: Solving Linear Equations", "Practice Set: Equation Solving"]
},
"factor": {
"understanding": "Factoring can be tricky! Let's review the key strategies.",
"hint": "Look for common factors first, then check if it's a special pattern.",
"example": "For x² + 5x + 6, we need two numbers that multiply to 6 and add to 5: (x + 2)(x + 3)",
"practice": "Try factoring: x² + 7x + 12",
"resources": ["Interactive: Factoring Tool", "Guide: Factoring Strategies"]
}
}
# Analyze question keywords
response_key = "solve" if "solve" in question.lower() else "factor" if "factor" in question.lower() else "solve"
response = tutoring_responses[response_key]
return {
"student_id": student_id,
"question": question,
"subject": subject,
"response": response,
"hint_level": hint_level,
"follow_up_questions": [
"Do you understand this step?",
"Would you like to try another example?",
"Should we review the fundamentals?"
],
"session_id": f"TUT{datetime.now().strftime('%Y%m%d%H%M%S')}",
"timestamp": datetime.now().isoformat()
}
@tool(
schema={
"type": "function",
"function": {
"name": "track_progress",
"description": "Monitors completion rates, scores, time spent, and generates achievement reports",
"parameters": {
"type": "object",
"properties": {
"student_id": {
"type": "string",
"description": "Student identifier"
},
"subject": {
"type": "string",
"description": "Subject to track"
},
"timeframe": {
"type": "string",
"enum": ["week", "month", "semester", "year"],
"description": "Timeframe for progress report"
}
},
"required": ["student_id"]
}
}
}
)
async def track_progress(student_id: str, subject: str = "all", timeframe: str = "month") -> Dict[str, Any]:
"""Track and report student progress"""
return {
"student_id": student_id,
"subject": subject,
"timeframe": timeframe,
"progress_report": {
"overall_progress": 72,
"completion_rate": 85,
"average_score": 78,
"time_spent_hours": 45,
"topics_completed": 12,
"topics_remaining": 4,
"streak_days": 15,
"improvements": ["Problem solving speed +25%", "Accuracy +15%"],
"challenges": ["Complex word problems", "Time management"]
},
"achievements": [
{"badge": "Consistent Learner", "earned": "2025-01-10"},
{"badge": "Problem Solver", "earned": "2025-01-08"}
],
"recommendations": [
"Focus more on word problems",
"Try advanced difficulty content",
"Join study group for peer learning"
],
"next_milestone": "Complete Algebra Module",
"generated_at": datetime.now().isoformat()
}
@tool(
schema={
"type": "function",
"function": {
"name": "generate_quiz",
"description": "Creates adaptive assessments with multiple choice, open-ended, and word problems",
"parameters": {
"type": "object",
"properties": {
"student_id": {
"type": "string",
"description": "Student identifier"
},
"topic": {
"type": "string",
"description": "Quiz topic"
},
"num_questions": {
"type": "integer",
"description": "Number of questions"
},
"difficulty": {
"type": "string",
"enum": ["easy", "medium", "hard", "adaptive"],
"description": "Quiz difficulty"
}
},
"required": ["student_id", "topic"]
}
}
}
)
async def generate_quiz(student_id: str, topic: str, num_questions: int = 10, difficulty: str = "adaptive") -> Dict[str, Any]:
"""Generate personalized quiz"""
quiz_id = f"QUIZ{datetime.now().strftime('%Y%m%d%H%M%S')}"
sample_questions = [
{
"question_id": 1,
"question": "Solve for x: 2x + 5 = 15",
"type": "multiple_choice",
"options": ["x = 5", "x = 10", "x = 7.5", "x = 20"],
"difficulty": "easy",
"points": 10
},
{
"question_id": 2,
"question": "Factor: x² + 7x + 12",
"type": "open_ended",
"difficulty": "medium",
"points": 15
},
{
"question_id": 3,
"question": "A train travels 120 miles in 2 hours. What is its average speed?",
"type": "word_problem",
"difficulty": "medium",
"points": 20
}
]
return {
"quiz_id": quiz_id,
"student_id": student_id,
"topic": topic,
"questions": sample_questions[:num_questions],
"total_points": sum(q["points"] for q in sample_questions[:num_questions]),
"time_limit": num_questions * 3, # 3 minutes per question
"difficulty": difficulty,
"adaptive_enabled": difficulty == "adaptive",
"created_at": datetime.now().isoformat(),
"expires_at": (datetime.now() + timedelta(hours=24)).isoformat()
}
# ============================================
# AGENT SYSTEM SETUP
# ============================================
class EducationalLearningSystem:
"""Complete educational learning multi-agent system"""
def __init__(self, api_key: str):
self.api_key = api_key
self.manager = None
self.agents = {}
def setup(self):
"""Setup the multi-agent hierarchy"""
# Create Manager Agent - Education Director
self.manager = ManagerAgent(
name="education_director",
api_key=self.api_key,
model="usf-mini",
backstory="""I am an experienced Education Director with 20 years in
curriculum development and personalized learning. I specialize in creating
adaptive learning experiences that maximize student potential.""",
goal="Provide personalized, effective learning experiences for every student",
instruction="""
1. Assess student learning needs and preferences
2. Design personalized curriculum paths
3. Coordinate with subject specialists and tutors
4. Monitor student progress and adapt strategies
5. Provide feedback to students and parents
6. Ensure engaging and effective learning outcomes
7. Maintain educational standards and best practices
""",
knowledge_cutoff="15 January 2025",
temperature=0.6,
max_iterations=25
)
# Register manager tools
self.manager.register_tool(assess_student_level)
self.manager.register_tool(create_learning_path)
# Create Level 1 Workers
# Curriculum Designer
curriculum_designer = self.manager.create_worker(
name="Curriculum_Designer",
role="Designs personalized learning paths and selects educational content",
backstory="Expert in curriculum design and learning path optimization",
goal="Create effective, engaging curriculum tailored to individual needs",
instruction="Design personalized learning paths based on student assessment"
)
curriculum_designer.register_tool(create_learning_path)
curriculum_designer.register_tool(recommend_content)
self.agents["curriculum_designer"] = curriculum_designer
# Assessment Specialist
assessment_specialist = self.manager.create_worker(
name="Assessment_Specialist",
role="Evaluates student performance and creates diagnostic assessments",
backstory="Specialist in educational assessment and learning analytics",
goal="Accurately assess student knowledge and learning progress",
instruction="Evaluate student performance and identify learning gaps"
)
assessment_specialist.register_tool(assess_student_level)
assessment_specialist.register_tool(generate_quiz)
assessment_specialist.register_tool(track_progress)
self.agents["assessment_specialist"] = assessment_specialist
# Learning Coach
learning_coach = self.manager.create_worker(
name="Learning_Coach",
role="Provides personalized tutoring and motivational support to students",
backstory="Experienced educator focused on student motivation and support",
goal="Guide and support students through their learning journey",
instruction="Provide personalized coaching and learning support"
)
learning_coach.register_tool(provide_tutoring)
self.agents["learning_coach"] = learning_coach
# Create Level 2 Workers (Subject Tutors under Learning Coach)
# Math Tutor
math_tutor = learning_coach.create_worker(
name="Math_Tutor",
role="Teaches mathematical concepts and problem-solving techniques",
backstory="Expert math educator with deep subject knowledge",
goal="Help students master mathematical concepts and problem-solving"
)
math_tutor.register_tool(provide_tutoring)
math_tutor.register_tool(generate_quiz)
# Science Tutor
science_tutor = learning_coach.create_worker(
name="Science_Tutor",
role="Explains scientific concepts through hands-on learning approaches",
backstory="Experienced science teacher with hands-on learning approach",
goal="Make science engaging and understandable"
)
science_tutor.register_tool(provide_tutoring)
science_tutor.register_tool(recommend_content)
# Language Tutor
language_tutor = learning_coach.create_worker(
name="Language_Tutor",
role="Develops reading, writing, and communication skills",
backstory="Expert in language learning and literacy development",
goal="Develop strong communication and language skills"
)
language_tutor.register_tool(provide_tutoring)
language_tutor.register_tool(recommend_content)
# Create Level 2 Workers (under Curriculum Designer)
# Content Curator
content_curator = curriculum_designer.create_worker(
name="Content_Curator",
role="Selects and organizes high-quality educational materials",
backstory="Expert in educational content selection and curation",
goal="Provide high-quality, engaging learning materials"
)
content_curator.register_tool(recommend_content)
# Create Level 2 Workers (under Assessment Specialist)
# Quiz Master
quiz_master = assessment_specialist.create_worker(
name="Quiz_Master",
role="Creates adaptive quizzes and formative assessments",
backstory="Specialist in creating effective assessments and quizzes",
goal="Design assessments that accurately measure learning"
)
quiz_master.register_tool(generate_quiz)
# Progress Tracker
progress_tracker = assessment_specialist.create_worker(
name="Progress_Tracker",
role="Analyzes learning metrics and generates progress reports",
backstory="Expert in tracking and analyzing learning progress",
goal="Monitor and report on student achievement"
)
progress_tracker.register_tool(track_progress)
# Parent Liaison (under Progress Tracker)
parent_liaison = progress_tracker.create_worker(
name="Parent_Liaison",
role="Communicates student progress and engages parents in learning process",
backstory="Experienced in parent engagement and communication",
goal="Keep parents informed and involved in student learning"
)
print("✅ Educational Learning System initialized successfully!")
# Visualize hierarchy
inspector = PipelineInspector(self.manager)
print("\n📊 EDUCATION TEAM HIERARCHY:")
print(inspector.visualize(format='mermaid'))
print("\n🔧 EDUCATIONAL TOOLS AVAILABLE:")
# Get all tools from inspector
for agent in inspector.pipeline_data['agents']:
if agent['tools']:
print(f" {agent['name']}:")
for tool_name in agent['tools']:
print(f" • {tool_name}")
async def assist_student(self, request: str, student_id: str = "STU001"):
"""Process student learning request"""
messages = [
{
"role": "system",
"content": f"Student ID: {student_id}\nDate: {datetime.now().strftime('%Y-%m-%d %H:%M')}\nLearning Session Active"
},
{
"role": "user",
"content": request
}
]
print(f"\n🎓 Student ({student_id}): {request}")
print("📚 Processing with educational AI system...")
try:
response = await self.manager.execute(messages)
if response and "choices" in response:
result = response["choices"][0]["message"]["content"]
print(f"\n✅ Learning Assistant Response:\n{result}")
return {
"success": True,
"student_id": student_id,
"request": request,
"response": result,
"timestamp": datetime.now().isoformat()
}
except Exception as e:
print(f"❌ Error: {str(e)}")
return {
"success": False,
"student_id": student_id,
"request": request,
"error": str(e),
"timestamp": datetime.now().isoformat()
}
# ============================================
# EXAMPLE USAGE
# ============================================
async def run_education_example():
"""Run educational learning scenarios"""
# Initialize system
education_system = EducationalLearningSystem(API_KEY)
education_system.setup()
# Test scenarios
test_cases = [
{
"student_id": "STU001",
"request": "I'm struggling with algebra, especially solving equations. Can you create a personalized learning plan for me? I have about 4 weeks to prepare for my exam."
},
{
"student_id": "STU002",
"request": "I need help with this problem: Solve for x: 3x + 7 = 22. Can you walk me through it step by step?"
}
]
print("\n" + "="*60)
print("📚 RUNNING EDUCATIONAL LEARNING SCENARIOS")
print("="*60)
results = []
for test_case in test_cases[:1]: # Run first case for demo
result = await education_system.assist_student(
test_case["request"],
test_case["student_id"]
)
results.append(result)
print("\n" + "-"*60)
return results
# ============================================
# MAIN EXECUTION
# ============================================
# Run the example
results = await run_education_example()
# Display results
print("\n📋 LEARNING ASSISTANCE RESULTS:")
for result in results:
print(json.dumps(result, indent=2))
Expected Output
✅ Educational Learning System initialized successfully!
📊 EDUCATION TEAM HIERARCHY:
graph TB
Education_Director["Education Director"]
Curriculum_Designer["Curriculum Designer"]
Assessment_Specialist["Assessment Specialist"]
Learning_Coach["Learning Coach"]
Math_Tutor["Math Tutor"]
Science_Tutor["Science Tutor"]
Language_Tutor["Language Tutor"]
Content_Curator["Content Curator"]
Quiz_Master["Quiz Master"]
Progress_Tracker["Progress Tracker"]
Parent_Liaison["Parent Liaison"]
Education_Director --> Curriculum_Designer
Education_Director --> Assessment_Specialist
Education_Director --> Learning_Coach
Learning_Coach --> Math_Tutor
Learning_Coach --> Science_Tutor
Learning_Coach --> Language_Tutor
Curriculum_Designer --> Content_Curator
Assessment_Specialist --> Quiz_Master
Assessment_Specialist --> Progress_Tracker
Progress_Tracker --> Parent_Liaison
🔧 EDUCATIONAL TOOLS AVAILABLE:
• assess_student_level
• create_learning_path
• recommend_content
• provide_tutoring
• track_progress
• generate_quiz
============================================================
📚 RUNNING EDUCATIONAL LEARNING SCENARIOS
============================================================
🎓 Student (STU001): I'm struggling with algebra, especially solving equations. Can you create a personalized learning plan for me? I have about 4 weeks to prepare for my exam.
📚 Processing with educational AI system...
✅ Learning Assistant Response:
I understand you're struggling with algebra and need to prepare for your exam in 4 weeks. Let me create a personalized learning plan tailored to your needs.
📊 ASSESSMENT RESULTS:
Based on your profile, I've identified:
• Current Level: Grade 8 Mathematics
• Proficiency: 75%
• Strengths: Basic algebra and geometry
• Areas for Improvement: Equation solving and word problems
• Learning Style: Visual learner with preference for interactive content
📚 YOUR 4-WEEK PERSONALIZED LEARNING PLAN:
WEEK 1: ALGEBRAIC EXPRESSIONS & BASIC EQUATIONS
• Topics: Simplifying expressions, solving linear equations
• Daily Commitment: 1-1.5 hours
• Resources:
- Video: "Introduction to Algebra" (15 min)
- Interactive practice: 25 adaptive problems daily
- Algebra Adventure Game for reinforcement
• Goal: Master basic equation solving
WEEK 2: ADVANCED EQUATIONS & SYSTEMS
• Topics: Multi-step equations, systems of equations
• Daily Commitment: 1.5 hours
• Resources:
- Step-by-step tutorials with worked examples
- Practice sets with immediate feedback
- Peer study sessions (optional)
• Goal: Solve complex equations confidently
WEEK 3: WORD PROBLEMS & APPLICATIONS
• Topics: Translating words to equations, real-world problems
• Daily Commitment: 1.5-2 hours
• Resources:
- Word problem strategy guide
- Context-based practice scenarios
- Video walkthroughs of common problem types
• Goal: Apply algebra to practical situations
WEEK 4: REVIEW & EXAM PREPARATION
• Topics: Comprehensive review, practice exams
• Daily Commitment: 2 hours
• Resources:
- Summary sheets and formula reference
- 3 full practice exams with solutions
- One-on-one tutoring sessions available
• Goal: Consolidate learning and build confidence
📈 SUPPORT FEATURES:
✓ 24/7 AI tutor available for questions
✓ Progress tracking with daily reports
✓ Adaptive difficulty adjustment
✓ Achievement badges for motivation
✓ Parent progress updates (optional)
Ready to start? I'll guide you through each step and adjust the plan based on your progress. You've got this! 💪
📋 LEARNING ASSISTANCE RESULTS:
{
"success": true,
"student_id": "STU001",
"request": "I'm struggling with algebra...",
"response": "I understand you're struggling...",
"timestamp": "2025-01-12T12:30:45.123456"
}
Customization Options
1. Add Specialized Learning Support
# Add Special Education Support
special_ed_specialist = manager.create_worker(
name="Special_Education_Specialist",
role="Special Needs Education Expert",
backstory="Expert in adaptive learning for diverse needs",
goal="Provide inclusive education support"
)
2. Integrate Gamification
@tool(schema={...})
async def create_learning_game(topic: str, difficulty: str):
"""Create educational game for topic"""
return {"game_url": game_link, "xp_rewards": points}
3. Add Collaborative Learning
@tool(schema={...})
async def create_study_group(student_ids: List[str], topic: str):
"""Form study groups for peer learning"""
return {"group_id": group_id, "schedule": meeting_times}
Production Considerations
- Privacy Compliance: COPPA and FERPA compliance for student data
- Accessibility: Support for diverse learning needs and disabilities
- Content Quality: Vetted, age-appropriate educational content
- Progress Analytics: Detailed learning analytics for educators
- Parent Portal: Secure access for parent monitoring
- Integration: LMS and school system integration capabilities
- Offline Support: Downloadable content for offline learning
- Multi-language: Support for multiple languages and ESL learners