modern-redesign-online-language-platform

Imported from wojciechwiesner/modern-redesign-online-language-platform (AGENTS.md). Install upstream with npx skills add wojciechwiesner/modern-redesign-online-language-platform. Copyright stays with the author.

🤖 AI Agents Documentation

Overview

This document describes the AI agents and automated systems that could be implemented to enhance the Akademia Poliglotki platform. The agents are designed to improve user experience, automate routine tasks, and provide intelligent assistance throughout the language learning journey.

🎯 Agent Architecture

Core Agent Types

1. Learning Assistant Agent

• Purpose: Personalized learning guidance and support
• Capabilities:
  • Assess student proficiency level
  • Recommend appropriate learning materials
  • Track progress and suggest next steps
  • Provide motivational feedback
• Integration: Chat interface, dashboard widgets
• Technology: Natural Language Processing, Machine Learning

2. Scheduling Agent

• Purpose: Automate lesson booking and calendar management
• Capabilities:
  • Find optimal lesson times based on preferences
  • Handle rescheduling requests
  • Send reminders and confirmations
  • Manage teacher availability
• Integration: Calendar system, email notifications
• Technology: Calendar APIs, Automated workflows

3. Content Recommendation Agent

• Purpose: Suggest relevant learning materials and exercises
• Capabilities:
  • Analyze learning patterns
  • Recommend courses and materials
  • Suggest practice exercises
  • Curate personalized content feeds
• Integration: Course catalog, material library
• Technology: Recommendation algorithms, Content analysis

4. Support Agent

• Purpose: Handle customer inquiries and technical support
• Capabilities:
  • Answer frequently asked questions
  • Troubleshoot technical issues
  • Escalate complex problems to human staff
  • Provide platform navigation help
• Integration: Help desk, live chat
• Technology: Conversational AI, Knowledge base

🛠️ Implementation Strategy

Phase 1: Enhanced Chatbot (Current - v1.2.0)

// Current Enhanced Chatbot Component
const Chatbot = () => {
  const [isOpen, setIsOpen] = useState(false);
  const [messages, setMessages] = useState([initialMessage]);
  const [inputText, setInputText] = useState('');
  const [isLoading, setIsLoading] = useState(false);
  const [showSuggestions, setShowSuggestions] = useState(true);

  const suggestedQuestions = [
    "Jak zacząć naukę angielskiego?",
    "Ile kosztują lekcje?",
    "Czy oferujecie bezpłatną lekcję próbną?",
    // ... more suggestions
  ];

  const sendMessage = async (messageText = null) => {
    // Try AI service first, fallback to pre-configured responses
    try {
      const response = await ollamaAPI.generate(prompt);
      // Handle AI response
    } catch (error) {
      // Intelligent fallback system with keyword matching
      const fallbackResponse = generateFallbackResponse(messageText);
      // Return contextual response
    }
  };
};

Current Features (v1.2.0):

• ✅ Advanced chat interface with suggested questions
• ✅ AI integration with Ollama backend
• ✅ Intelligent fallback system for offline functionality
• ✅ Professional styling with glassmorphism effects
• ✅ Keyword-based response matching
• ✅ Integration with FAQ section
• ✅ Mobile-responsive design
• ✅ Loading states and error handling

Recent Improvements:

• ✅ Suggested questions for quick interaction
• ✅ Fallback responses when AI is unavailable
• ✅ Professional error messages with contact info
• ✅ FAQ integration button
• ✅ Auto-hide suggestions after first interaction
• ✅ Smart keyword matching for relevant responses

Phase 2: Intelligent Conversational Agent

// Enhanced AI-Powered Chatbot
const SmartChatbot = () => {
  const [conversation, setConversation] = useState([]);
  const [userContext, setUserContext] = useState({});
  
  const processMessage = async (userInput) => {
    // Natural language understanding
    const intent = await analyzeIntent(userInput);
    const context = await getUserContext();
    
    // Generate contextual response
    const response = await generateResponse(intent, context);
    
    // Update conversation history
    updateConversationHistory(userInput, response);
    
    return response;
  };
};

Enhanced Features:

• 🧠 Natural language processing
• 🎯 Intent recognition
• 👤 User context awareness
• 📚 Learning material integration

Phase 3: Multi-Agent System

interface AgentSystem {
  learningAgent: LearningAssistant;
  schedulingAgent: SchedulingAssistant;
  contentAgent: ContentRecommendation;
  supportAgent: CustomerSupport;
}

class AgentOrchestrator {
  async routeRequest(userInput: string, context: UserContext) {
    const intent = await this.classifyIntent(userInput);
    
    switch(intent.category) {
      case 'learning':
        return this.learningAgent.handle(userInput, context);
      case 'scheduling':
        return this.schedulingAgent.handle(userInput, context);
      case 'content':
        return this.contentAgent.handle(userInput, context);
      case 'support':
        return this.supportAgent.handle(userInput, context);
    }
  }
}

🎨 Agent UI Components

Chat Interface Design

const AgentChatInterface = () => {
  return (
    <div className="fixed bottom-4 right-4 z-50">
      {/* Floating Chat Button */}
      <button className="bg-gradient-to-r from-blue-500 to-indigo-600 rounded-full p-4 shadow-lg hover:scale-105 transition-transform">
        <MessageCircle className="w-6 h-6 text-white" />
      </button>
      
      {/* Chat Window */}
      <div className="bg-white/95 backdrop-blur-xl rounded-2xl shadow-xl border border-white/20 max-w-md">
        {/* Header */}
        <div className="p-4 border-b border-gray-200/50">
          <h3 className="font-semibold text-gray-800">Asystent Nauki</h3>
          <p className="text-sm text-gray-600">Jestem tutaj, aby pomóc!</p>
        </div>
        
        {/* Messages */}
        <div className="h-96 p-4 overflow-y-auto">
          {messages.map((message, index) => (
            <MessageBubble key={index} message={message} />
          ))}
        </div>
        
        {/* Input */}
        <div className="p-4 border-t border-gray-200/50">
          <input 
            type="text" 
            placeholder="Napisz wiadomość..."
            className="w-full p-3 rounded-xl border border-gray-200 focus:outline-none focus:ring-2 focus:ring-blue-500"
          />
        </div>
      </div>
    </div>
  );
};

Agent Status Indicators

const AgentStatusPanel = () => {
  return (
    <div className="grid grid-cols-2 lg:grid-cols-4 gap-4">
      {/* Learning Agent */}
      <div className="bg-green-50 border border-green-200 rounded-xl p-4">
        <div className="flex items-center space-x-2 mb-2">
          <div className="w-3 h-3 bg-green-500 rounded-full animate-pulse"></div>
          <span className="text-green-800 font-medium">Asystent Nauki</span>
        </div>
        <p className="text-sm text-green-600">Aktywny - gotowy do pomocy</p>
      </div>
      
      {/* Scheduling Agent */}
      <div className="bg-blue-50 border border-blue-200 rounded-xl p-4">
        <div className="flex items-center space-x-2 mb-2">
          <div className="w-3 h-3 bg-blue-500 rounded-full animate-pulse"></div>
          <span className="text-blue-800 font-medium">Kalendarz</span>
        </div>
        <p className="text-sm text-blue-600">Monitoring terminów</p>
      </div>
      
      {/* Content Agent */}
      <div className="bg-purple-50 border border-purple-200 rounded-xl p-4">
        <div className="flex items-center space-x-2 mb-2">
          <div className="w-3 h-3 bg-purple-500 rounded-full animate-pulse"></div>
          <span className="text-purple-800 font-medium">Rekomendacje</span>
        </div>
        <p className="text-sm text-purple-600">Analizuje preferencje</p>
      </div>
      
      {/* Support Agent */}
      <div className="bg-orange-50 border border-orange-200 rounded-xl p-4">
        <div className="flex items-center space-x-2 mb-2">
          <div className="w-3 h-3 bg-orange-500 rounded-full animate-pulse"></div>
          <span className="text-orange-800 font-medium">Wsparcie</span>
        </div>
        <p className="text-sm text-orange-600">Dostępny 24/7</p>
      </div>
    </div>
  );
};

🔧 Technical Implementation

AI/ML Stack Options

Option 1: Cloud-Based AI Services

// Using OpenAI API for conversational AI
const openaiConfig = {
  apiKey: process.env.OPENAI_API_KEY,
  model: "gpt-4-turbo",
  temperature: 0.7
};

const generateResponse = async (userMessage, context) => {
  const completion = await openai.chat.completions.create({
    model: "gpt-4-turbo",
    messages: [
      {
        role: "system",
        content: "You are a helpful language learning assistant for Akademia Poliglotki..."
      },
      {
        role: "user", 
        content: userMessage
      }
    ],
    context: context
  });
  
  return completion.choices[0].message.content;
};

Option 2: Local AI Models

// Using Hugging Face Transformers
const pipeline = require('@xenova/transformers');

const classifier = await pipeline('text-classification', 
  'microsoft/DialoGPT-medium');

const generateLocalResponse = async (input) => {
  const result = await classifier(input);
  return processAndFormatResponse(result);
};

Option 3: Hybrid Approach

// Combine local processing with cloud AI
const hybridAgent = {
  // Quick responses from local models
  handleSimpleQueries: (input) => localNLP.process(input),
  
  // Complex queries to cloud AI
  handleComplexQueries: (input) => cloudAI.process(input),
  
  // Route based on confidence score
  routeQuery: (input) => {
    const confidence = localNLP.getConfidence(input);
    return confidence > 0.8 ? 
      this.handleSimpleQueries(input) : 
      this.handleComplexQueries(input);
  }
};

Data Integration Points

User Context API

interface UserContext {
  userId: string;
  currentLevel: string;
  learningGoals: string[];
  preferredLanguages: string[];
  schedule: {
    availableHours: number[];
    timezone: string;
  };
  progress: {
    completedLessons: number;
    currentStreak: number;
    weakAreas: string[];
  };
}

const getUserContext = async (userId: string): Promise<UserContext> => {
  // Fetch from database/API
  const userData = await api.get(`/users/${userId}/context`);
  return userData;
};

Learning Analytics

interface LearningAnalytics {
  trackEvent: (event: string, properties: object) => void;
  getInsights: (userId: string) => Promise<Insights>;
  generateRecommendations: (context: UserContext) => Promise<Recommendation[]>;
}

const analytics = {
  trackLessonStart: (lessonId, userId) => {
    analytics.trackEvent('lesson_started', {
      lessonId,
      userId,
      timestamp: Date.now()
    });
  },
  
  trackProgress: (userId, score) => {
    analytics.trackEvent('progress_update', {
      userId,
      score,
      timestamp: Date.now()
    });
  }
};

📊 Agent Performance Metrics

Key Performance Indicators (KPIs)

1. Response Quality Metrics

const qualityMetrics = {
  // User satisfaction ratings
  satisfactionScore: 4.2, // out of 5
  
  // Response relevance
  relevanceScore: 0.87, // 0-1 scale
  
  // Resolution rate
  firstContactResolution: 0.73, // 73% resolved in first interaction
  
  // Response time
  averageResponseTime: 1.2 // seconds
};

2. Learning Effectiveness Metrics

const learningMetrics = {
  // Student engagement
  sessionDuration: 25.3, // minutes average
  lessonsPerWeek: 3.1,
  
  // Learning outcomes
  proficiencyImprovement: 0.23, // 23% improvement rate
  goalAchievement: 0.68, // 68% of students reach goals
  
  // Retention
  monthlyRetention: 0.84, // 84% active after 1 month
  completionRate: 0.76 // 76% complete courses
};

3. System Performance Metrics

const systemMetrics = {
  // Availability
  uptime: 0.997, // 99.7% uptime
  
  // Performance
  averageLatency: 120, // milliseconds
  throughput: 1000, // requests per minute
  
  // Errors
  errorRate: 0.003, // 0.3% error rate
  
  // Scalability
  concurrentUsers: 500, // max concurrent users handled
  peakLoad: 50 // requests per second peak
};

🚀 Deployment Strategy

Infrastructure Requirements

# Docker Compose for Agent Services
version: '3.8'
services:
  chatbot-api:
    image: akademia-poliglotki/chatbot:latest
    ports:
      - "3001:3000"
    environment:
      - AI_SERVICE_URL=https://api.openai.com
      - DATABASE_URL=postgresql://...
    
  scheduling-agent:
    image: akademia-poliglotki/scheduler:latest
    ports:
      - "3002:3000"
    environment:
      - CALENDAR_API_KEY=${CALENDAR_API_KEY}
    
  analytics-service:
    image: akademia-poliglotki/analytics:latest
    ports:
      - "3003:3000"
    
  redis-cache:
    image: redis:alpine
    ports:
      - "6379:6379"

Cloud Deployment Options

AWS Infrastructure

# Terraform configuration for AWS
resource "aws_ecs_cluster" "agent_cluster" {
  name = "akademia-poliglotki-agents"
}

resource "aws_ecs_service" "chatbot_service" {
  name            = "chatbot"
  cluster         = aws_ecs_cluster.agent_cluster.id
  task_definition = aws_ecs_task_definition.chatbot.arn
  desired_count   = 2
}

resource "aws_lambda_function" "recommendation_engine" {
  filename         = "recommendation-engine.zip"
  function_name    = "recommendation-engine"
  role            = aws_iam_role.lambda_role.arn
  handler         = "index.handler"
  runtime         = "nodejs18.x"
}

🔐 Security & Privacy

Data Protection Measures

const securityConfig = {
  // Data encryption
  encryption: {
    algorithm: 'AES-256-GCM',
    keyRotation: '30days'
  },
  
  // Access control
  authentication: {
    method: 'JWT',
    expiration: '24h',
    refreshToken: true
  },
  
  // Privacy compliance
  gdpr: {
    dataRetention: '2years',
    rightToErasure: true,
    consentManagement: true
  },
  
  // API security
  rateLimiting: {
    requests: 100,
    windowMs: 15 * 60 * 1000 // 15 minutes
  }
};

User Consent Management

const ConsentManager = () => {
  const [consents, setConsents] = useState({
    aiAssistant: false,
    analytics: false,
    personalization: false
  });
  
  const handleConsentChange = (type, value) => {
    setConsents(prev => ({
      ...prev,
      [type]: value
    }));
    
    // Update user preferences
    updateUserConsent(type, value);
  };
  
  return (
    <div className="bg-white rounded-xl shadow-lg p-6">
      <h3 className="text-lg font-semibold mb-4">Ustawienia Prywatności</h3>
      
      <div className="space-y-4">
        <ConsentToggle
          label="Asystent AI"
          description="Pozwól asystentowi AI analizować Twoje postępy"
          checked={consents.aiAssistant}
          onChange={(value) => handleConsentChange('aiAssistant', value)}
        />
        
        <ConsentToggle
          label="Analityka"
          description="Pomóż nam ulepszyć platformę poprzez anonimowe dane"
          checked={consents.analytics}
          onChange={(value) => handleConsentChange('analytics', value)}
        />
        
        <ConsentToggle
          label="Personalizacja"
          description="Otrzymuj spersonalizowane rekomendacje treści"
          checked={consents.personalization}
          onChange={(value) => handleConsentChange('personalization', value)}
        />
      </div>
    </div>
  );
};

📈 Future Roadmap

Short-term Goals (3-6 months)

• ✅ Enhanced chatbot implementation with AI integration
• ✅ Suggested questions and fallback responses
• ✅ FAQ section integration
• 🔄 Advanced natural language processing
• 🔄 User context awareness
• 🔄 Simple recommendation engine

Medium-term Goals (6-12 months)

• 🔄 Multi-agent system deployment
• 🔄 Advanced analytics dashboard
• 🔄 Voice interaction capabilities
• 🔄 Mobile app integration

Long-term Vision (12+ months)

• 🔄 Predictive learning paths
• 🔄 Emotion recognition for engagement
• 🔄 Virtual reality language immersion
• 🔄 Advanced gamification with AI

📞 Technical Support

Agent Monitoring Dashboard

const AgentMonitoringDashboard = () => {
  return (
    <div className="grid grid-cols-1 lg:grid-cols-3 gap-6">
      {/* System Health */}
      <MetricCard
        title="System Health"
        value="99.7%"
        trend="+0.2%"
        color="green"
      />
      
      {/* Active Conversations */}
      <MetricCard
        title="Active Chats"
        value="23"
        trend="+5"
        color="blue"
      />
      
      {/* Response Time */}
      <MetricCard
        title="Avg Response Time"
        value="1.2s"
        trend="-0.3s"
        color="purple"
      />
    </div>
  );
};

Troubleshooting Guide

1. Agent Not Responding: Check API connection and rate limits
2. Slow Response Times: Verify server load and database performance
3. Incorrect Recommendations: Review user context data and model training
4. Integration Issues: Validate API keys and webhook configurations

Status: 🔧 READY FOR IMPLEMENTATION

This agent system provides a comprehensive foundation for intelligent automation in the Akademia Poliglotki platform, enhancing user experience while maintaining security and scalability.