The Rise of EdTech in 2025: Transforming Education Through Technology Innovation

import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
import json

class LearningStyle(Enum):
    VISUAL = "visual"
    AUDITORY = "auditory"
    KINESTHETIC = "kinesthetic"
    READING_WRITING = "reading_writing"

class DifficultyLevel(Enum):
    BEGINNER = 1
    INTERMEDIATE = 2
    ADVANCED = 3
    EXPERT = 4

class ContentType(Enum):
    VIDEO = "video"
    TEXT = "text"
    INTERACTIVE = "interactive"
    QUIZ = "quiz"
    SIMULATION = "simulation"

@dataclass
class LearningObjective:
    objective_id: str
    title: str
    description: str
    subject: str
    difficulty_level: DifficultyLevel
    estimated_duration: int  # minutes
    prerequisites: List[str] = field(default_factory=list)
    skills_gained: List[str] = field(default_factory=list)

@dataclass
class StudentProfile:
    student_id: str
    name: str
    age: int
    grade_level: str
    learning_style: LearningStyle
    strengths: List[str] = field(default_factory=list)
    challenges: List[str] = field(default_factory=list)
    interests: List[str] = field(default_factory=list)
    learning_pace: float = 1.0  # 1.0 = average pace
    attention_span: int = 30  # minutes
    preferred_content_types: List[ContentType] = field(default_factory=list)

@dataclass
class LearningSession:
    session_id: str
    student_id: str
    objective_id: str
    start_time: datetime
    end_time: Optional[datetime] = None
    completion_rate: float = 0.0
    engagement_score: float = 0.0
    performance_score: float = 0.0
    content_consumed: List[str] = field(default_factory=list)
    interactions: int = 0
    hints_used: int = 0
    mistakes_made: int = 0

class PersonalizedLearningPlatform:
    def __init__(self):
        self.students = {}
        self.learning_objectives = {}
        self.content_library = {}
        self.learning_sessions = []
        self.ai_models = self._initialize_ai_models()
        self.adaptation_rules = self._load_adaptation_rules()
        
    def _initialize_ai_models(self) -> Dict:
        """Initialize AI models for various learning tasks"""
        return {
            'learning_style_detector': {
                'accuracy': 0.87,
                'confidence_threshold': 0.8,
                'training_samples': 50000
            },
            'difficulty_predictor': {
                'accuracy': 0.92,
                'features': ['prior_performance', 'time_spent', 'error_patterns'],
                'model_type': 'gradient_boosting'
            },
            'engagement_analyzer': {
                'accuracy': 0.84,
                'inputs': ['click_patterns', 'session_duration', 'completion_rates'],
                'model_type': 'neural_network'
            },
            'content_recommender': {
                'algorithm': 'collaborative_filtering',
                'similarity_threshold': 0.75,
                'recommendation_pool_size': 10
            }
        }
    
    def _load_adaptation_rules(self) -> Dict:
        """Load rules for adaptive learning adjustments"""
        return {
            'difficulty_adjustment': {
                'increase_threshold': 0.85,  # Success rate above 85%
                'decrease_threshold': 0.60,  # Success rate below 60%
                'adjustment_factor': 0.2
            },
            'content_switching': {
                'low_engagement_threshold': 0.3,
                'alternative_content_types': {
                    ContentType.VIDEO: [ContentType.INTERACTIVE, ContentType.TEXT],
                    ContentType.TEXT: [ContentType.VIDEO, ContentType.INTERACTIVE],
                    ContentType.INTERACTIVE: [ContentType.VIDEO, ContentType.SIMULATION]
                }
            },
            'pacing_adjustment': {
                'fast_learner_threshold': 1.5,  # Completes 50% faster than average
                'slow_learner_threshold': 0.7,  # Takes 30% longer than average
                'break_recommendation_interval': 45  # minutes
            }
        }
    
    def register_student(self, profile: StudentProfile) -> Dict:
        """Register a new student and initialize their learning profile"""
        self.students[profile.student_id] = profile
        
        # Initialize learning analytics
        learning_analytics = {
            'total_learning_time': 0,
            'objectives_completed': 0,
            'average_performance': 0.0,
            'learning_streak': 0,
            'last_active': datetime.now(),
            'progress_by_subject': {},
            'learning_velocity': 1.0,  # Objectives per hour
            'retention_rate': 0.0,
            'engagement_trend': []
        }
        
        # Detect initial learning style if not provided
        if not profile.learning_style:
            profile.learning_style = self._detect_learning_style(profile)
        
        return {
            'student_id': profile.student_id,
            'registration_status': 'success',
            'initial_assessment_required': True,
            'recommended_starting_level': self._recommend_starting_level(profile),
            'analytics': learning_analytics
        }
    
    def _detect_learning_style(self, profile: StudentProfile) -> LearningStyle:
        """Use AI to detect student's learning style based on profile data"""
        # Simplified learning style detection based on profile information
        style_scores = {
            LearningStyle.VISUAL: 0,
            LearningStyle.AUDITORY: 0,
            LearningStyle.KINESTHETIC: 0,
            LearningStyle.READING_WRITING: 0
        }
        
        # Analyze interests for learning style indicators
        visual_interests = ['art', 'design', 'graphics', 'videos']
        auditory_interests = ['music', 'podcasts', 'discussions', 'languages']
        kinesthetic_interests = ['sports', 'experiments', 'building', 'hands-on']
        reading_interests = ['books', 'writing', 'research', 'literature']
        
        for interest in profile.interests:
            if interest.lower() in visual_interests:
                style_scores[LearningStyle.VISUAL] += 1
            elif interest.lower() in auditory_interests:
                style_scores[LearningStyle.AUDITORY] += 1
            elif interest.lower() in kinesthetic_interests:
                style_scores[LearningStyle.KINESTHETIC] += 1
            elif interest.lower() in reading_interests:
                style_scores[LearningStyle.READING_WRITING] += 1
        
        # Return the learning style with highest score
        return max(style_scores, key=style_scores.get)
    
    def _recommend_starting_level(self, profile: StudentProfile) -> DifficultyLevel:
        """Recommend appropriate starting difficulty level"""
        age_level_mapping = {
            (5, 8): DifficultyLevel.BEGINNER,
            (9, 12): DifficultyLevel.BEGINNER,
            (13, 16): DifficultyLevel.INTERMEDIATE,
            (17, 25): DifficultyLevel.INTERMEDIATE,
            (26, 100): DifficultyLevel.ADVANCED
        }
        
        for age_range, level in age_level_mapping.items():
            if age_range[0] <= profile.age <= age_range:
                return level
        
        return DifficultyLevel.INTERMEDIATE
    
    def create_personalized_learning_path(self, student_id: str, subject: str, 
                                         target_objectives: List[str]) -> Dict:
        """Create a personalized learning path for a student"""
        student = self.students.get(student_id)
        if not student:
            return {'error': 'Student not found'}
        
        # Analyze student's current knowledge and skills
        knowledge_gaps = self._identify_knowledge_gaps(student, subject)
        learning_preferences = self._analyze_learning_preferences(student)
        
        # Generate personalized learning sequence
        learning_path = {
            'student_id': student_id,
            'subject': subject,
            'created_at': datetime.now(),
            'estimated_completion_time': 0,
            'learning_sequence': [],
            'adaptive_checkpoints': [],
            'personalization_factors': {
                'learning_style': student.learning_style.value,
                'learning_pace': student.learning_pace,
                'attention_span': student.attention_span,
                'preferred_content_types': [ct.value for ct in student.preferred_content_types]
            }
        }
        
        # Build learning sequence
        for objective_id in target_objectives:
            objective = self.learning_objectives.get(objective_id)
            if not objective:
                continue
            
            # Check prerequisites
            if not self._check_prerequisites(student_id, objective.prerequisites):
                # Add prerequisite objectives first
                prerequisite_path = self._generate_prerequisite_path(student_id, objective.prerequisites)
                learning_path['learning_sequence'].extend(prerequisite_path)
            
            # Personalize content selection for this objective
            personalized_content = self._select_personalized_content(student, objective)
            
            learning_sequence_item = {
                'objective_id': objective_id,
                'title': objective.title,
                'estimated_duration': self._adjust_duration_for_student(objective.estimated_duration, student),
                'difficulty_level': objective.difficulty_level.value,
                'personalized_content': personalized_content,
                'success_criteria': self._define_success_criteria(objective, student),
                'adaptation_triggers': self._define_adaptation_triggers(objective)
            }
            
            learning_path['learning_sequence'].append(learning_sequence_item)
            learning_path['estimated_completion_time'] += learning_sequence_item['estimated_duration']
        
        # Add adaptive checkpoints
        learning_path['adaptive_checkpoints'] = self._generate_adaptive_checkpoints(
            learning_path['learning_sequence']
        )
        
        return learning_path
    
    def _identify_knowledge_gaps(self, student: StudentProfile, subject: str) -> List[str]:
        """Identify areas where student needs additional support"""
        # Analyze past performance to identify gaps
        student_sessions = [s for s in self.learning_sessions if s.student_id == student.student_id]
        
        gaps = []
        subject_sessions = [s for s in student_sessions 
                           if self.learning_objectives.get(s.objective_id, {}).get('subject') == subject]
        
        for session in subject_sessions:
            if session.performance_score < 0.7:  # Below 70% performance
                objective = self.learning_objectives.get(session.objective_id)
                if objective:
                    gaps.extend(objective.skills_gained)
        
        return list(set(gaps))  # Remove duplicates
    
    def _analyze_learning_preferences(self, student: StudentProfile) -> Dict:
        """Analyze student's learning preferences from past behavior"""
        student_sessions = [s for s in self.learning_sessions if s.student_id == student.student_id]
        
        if not student_sessions:
            return {
                'optimal_session_length': student.attention_span,
                'preferred_difficulty_progression': 'gradual',
                'engagement_patterns': [],
                'best_learning_times': []
            }
        
        # Analyze engagement patterns
        high_engagement_sessions = [s for s in student_sessions if s.engagement_score > 0.8]
        
        preferences = {
            'optimal_session_length': self._calculate_optimal_session_length(student_sessions),
            'preferred_difficulty_progression': self._analyze_difficulty_preferences(student_sessions),
            'engagement_patterns': self._identify_engagement_patterns(high_engagement_sessions),
            'best_learning_times': self._identify_optimal_learning_times(student_sessions)
        }
        
        return preferences
    
    def start_learning_session(self, student_id: str, objective_id: str) -> Dict:
        """Start a new personalized learning session"""
        student = self.students.get(student_id)
        objective = self.learning_objectives.get(objective_id)
        
        if not student or not objective:
            return {'error': 'Student or objective not found'}
        
        # Create new learning session
        session = LearningSession(
            session_id=f"session_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{student_id}",
            student_id=student_id,
            objective_id=objective_id,
            start_time=datetime.now()
        )
        
        # Personalize content based on student profile
        personalized_content = self._select_personalized_content(student, objective)
        
        session_config = {
            'session_id': session.session_id,
            'student_profile': {
                'learning_style': student.learning_style.value,
                'attention_span': student.attention_span,
                'learning_pace': student.learning_pace
            },
            'objective_details': {
                'title': objective.title,
                'difficulty_level': objective.difficulty_level.value,
                'estimated_duration': self._adjust_duration_for_student(objective.estimated_duration, student)
            },
            'personalized_content': personalized_content,
            'adaptive_features': {
                'real_time_difficulty_adjustment': True,
                'engagement_monitoring': True,
                'break_recommendations': True,
                'content_switching': True
            }
        }
        
        self.learning_sessions.append(session)
        
        return session_config
    
    def _select_personalized_content(self, student: StudentProfile, objective: LearningObjective) -> List[Dict]:
        """Select and order content based on student's learning profile"""
        # Get available content for this objective
        available_content = self.content_library.get(objective.objective_id, [])
        
        if not available_content:
            return []
        
        # Score content based on student preferences
        scored_content = []
        for content in available_content:
            score = self._calculate_content_score(student, content)
            scored_content.append((content, score))
        
        # Sort by score and return top content
        scored_content.sort(key=lambda x: x reverse=True)
        
        personalized_content = []
        for content, score in scored_content[:5]:  # Top 5 pieces of content
            personalized_content.append({
                'content_id': content['content_id'],
                'title': content['title'],
                'type': content['type'],
                'duration': content['duration'],
                'personalization_score': score,
                'adaptation_parameters': self._get_adaptation_parameters(student, content)
            })
        
        return personalized_content
    
    def _calculate_content_score(self, student: StudentProfile, content: Dict) -> float:
        """Calculate how well content matches student's learning profile"""
        score = 0.0
        
        # Learning style match
        content_type = ContentType(content['type'])
        style_preference_map = {
            LearningStyle.VISUAL: [ContentType.VIDEO, ContentType.INTERACTIVE],
            LearningStyle.AUDITORY: [ContentType.VIDEO],  # Videos often have audio
            LearningStyle.KINESTHETIC: [ContentType.INTERACTIVE, ContentType.SIMULATION],
            LearningStyle.READING_WRITING: [ContentType.TEXT, ContentType.QUIZ]
        }
        
        if content_type in style_preference_map.get(student.learning_style, []):
            score += 0.4
        
        # Duration match with attention span
        duration_ratio = content['duration'] / student.attention_span
        if 0.5 <= duration_ratio <= 1.0:  # Optimal duration range
            score += 0.3
        elif duration_ratio < 0.5:
            score += 0.2  # Short content is usually fine
        else:
            score += 0.1  # Long content may cause attention issues
        
        # Difficulty match
        if content.get('difficulty_level') == student.grade_level:
            score += 0.2
        
        # Interest alignment
        content_topics = content.get('topics', [])
        interest_overlap = len(set(content_topics) & set(student.interests))
        score += min(0.1, interest_overlap * 0.02)
        
        return min(1.0, score)
    
    def process_learning_interaction(self, session_id: str, interaction_data: Dict) -> Dict:
        """Process a learning interaction and adapt in real-time"""
        session = next((s for s in self.learning_sessions if s.session_id == session_id), None)
        if not session:
            return {'error': 'Session not found'}
        
        # Update session data
        session.interactions += 1
        session.content_consumed.append(interaction_data.get('content_id', ''))
        
        # Process specific interaction types
        if interaction_data.get('type') == 'answer_submitted':
            is_correct = interaction_data.get('is_correct', False)
            if not is_correct:
                session.mistakes_made += 1
        
        elif interaction_data.get('type') == 'hint_requested':
            session.hints_used += 1
        
        # Calculate real-time metrics
        engagement_score = self._calculate_engagement_score(session, interaction_data)
        performance_score = self._calculate_performance_score(session)
        
        session.engagement_score = engagement_score
        session.performance_score = performance_score
        
        # Determine if adaptation is needed
        adaptation_response = self._check_adaptation_triggers(session, interaction_data)
        
        return {
            'session_id': session_id,
            'engagement_score': engagement_score,
            'performance_score': performance_score,
            'adaptation_response': adaptation_response,
            'learning_insights': self._generate_learning_insights(session)
        }
    
    def _calculate_engagement_score(self, session: LearningSession, interaction_data: Dict) -> float:
        """Calculate student engagement score based on interaction patterns"""
        score = 0.0
        
        # Time spent vs expected time
        if session.start_time:
            time_spent = (datetime.now() - session.start_time).total_seconds() / 60
            objective = self.learning_objectives.get(session.objective_id)
            if objective:
                time_ratio = time_spent / objective.estimated_duration
                if 0.8 <= time_ratio <= 1.5:  # Appropriate time spent
                    score += 0.3
        
        # Interaction frequency
        if session.interactions > 0:
            interactions_per_minute = session.interactions / max(1, time_spent)
            if 1 <= interactions_per_minute <= 5:  # Healthy interaction rate
                score += 0.3
        
        # Content completion rate
        score += session.completion_rate * 0.4
        
        return min(1.0, score)
    
    def _calculate_performance_score(self, session: LearningSession) -> float:
        """Calculate student performance score"""
        if session.interactions == 0:
            return 0.0
        
        # Basic accuracy calculation
        correct_interactions = session.interactions - session.mistakes_made
        accuracy = correct_interactions / session.interactions
        
        # Adjust for hint usage (penalize excessive hint usage)
        hint_penalty = min(0.3, session.hints_used * 0.05)
        
        performance = max(0.0, accuracy - hint_penalty)
        
        return performance
    
    def _check_adaptation_triggers(self, session: LearningSession, interaction_data: Dict) -> Dict:
        """Check if learning adaptation is needed and return recommendations"""
        adaptations = []
        
        # Check engagement level
        if session.engagement_score < 0.3:
            adaptations.append({
                'type': 'engagement_boost',
                'action': 'switch_content_type',
                'reason': 'Low engagement detected',
                'priority': 'high'
            })
        
        # Check performance level
        if session.performance_score < 0.6 and session.interactions >= 5:
            adaptations.append({
                'type': 'difficulty_adjustment',
                'action': 'decrease_difficulty',
                'reason': 'Performance below threshold',
                'priority': 'medium'
            })
        elif session.performance_score > 0.9 and session.interactions >= 5:
            adaptations.append({
                'type': 'difficulty_adjustment',
                'action': 'increase_difficulty',
                'reason': 'High performance detected',
                'priority': 'low'
            })
        
        # Check session duration
        if session.start_time:
            session_duration = (datetime.now() - session.start_time).total_seconds() / 60
            student = self.students.get(session.student_id)
            if student and session_duration > student.attention_span:
                adaptations.append({
                    'type': 'break_recommendation',
                    'action': 'suggest_break',
                    'reason': 'Session duration exceeds attention span',
                    'priority': 'medium'
                })
        
        return {
            'adaptations_needed': len(adaptations) > 0,
            'adaptations': adaptations,
            'confidence': 0.8 if adaptations else 1.0
        }
    
    def generate_learning_analytics(self, student_id: str, time_period: int = 30) -> Dict:
        """Generate comprehensive learning analytics for a student"""
        student = self.students.get(student_id)
        if not student:
            return {'error': 'Student not found'}
        
        # Get sessions from the specified time period
        cutoff_date = datetime.now() - timedelta(days=time_period)
        recent_sessions = [s for s in self.learning_sessions 
                          if s.student_id == student_id and s.start_time >= cutoff_date]
        
        if not recent_sessions:
            return {'message': 'No recent learning activity found'}
        
        # Calculate analytics
        analytics = {
            'student_id': student_id,
            'analysis_period': f"Last {time_period} days",
            'generated_at': datetime.now(),
            'learning_summary': {
                'total_sessions': len(recent_sessions),
                'total_learning_time': sum((s.end_time - s.start_time).total_seconds() / 60 
                                         for s in recent_sessions if s.end_time),
                'average_session_duration': 0,
                'completion_rate': sum(s.completion_rate for s in recent_sessions) / len(recent_sessions),
                'average_performance': sum(s.performance_score for s in recent_sessions) / len(recent_sessions),
                'average_engagement': sum(s.engagement_score for s in recent_sessions) / len(recent_sessions)
            },
            'learning_patterns': self._analyze_learning_patterns(recent_sessions),
            'progress_tracking': self._track_learning_progress(recent_sessions),
            'recommendations': self._generate_learning_recommendations(student, recent_sessions)
        }
        
        # Calculate average session duration
        completed_sessions = [s for s in recent_sessions if s.end_time]
        if completed_sessions:
            total_duration = sum((s.end_time - s.start_time).total_seconds() / 60 for s in completed_sessions)
            analytics['learning_summary']['average_session_duration'] = total_duration / len(completed_sessions)
        
        return analytics
    
    # Helper methods for calculations
    def _adjust_duration_for_student(self, base_duration: int, student: StudentProfile) -> int:
        """Adjust content duration based on student's learning pace"""
        return int(base_duration / student.learning_pace)
    
    def _check_prerequisites(self, student_id: str, prerequisites: List[str]) -> bool:
        """Check if student has completed prerequisite objectives"""
        if not prerequisites:
            return True
        
        completed_objectives = set()
        for session in self.learning_sessions:
            if session.student_id == student_id and session.completion_rate >= 0.8:
                completed_objectives.add(session.objective_id)
        
        return all(prereq in completed_objectives for prereq in prerequisites)
    
    def _generate_learning_recommendations(self, student: StudentProfile, sessions: List[LearningSession]) -> List[str]:
        """Generate personalized learning recommendations"""
        recommendations = []
        
        if not sessions:
            recommendations.append("Start with assessment to identify your learning strengths")
            return recommendations
        
        avg_engagement = sum(s.engagement_score for s in sessions) / len(sessions)
        avg_performance = sum(s.performance_score for s in sessions) / len(sessions)
        
        if avg_engagement < 0.5:
            recommendations.append("Try different content types to boost engagement")
        
        if avg_performance < 0.7:
            recommendations.append("Consider reviewing prerequisite topics")
            recommendations.append("Take breaks between learning sessions")
        
        if avg_performance > 0.9:
            recommendations.append("Ready for more challenging content")
        
        # Add more specific recommendations based on learning patterns
        mistake_rate = sum(s.mistakes_made for s in sessions) / max(1, sum(s.interactions for s in sessions))
        if mistake_rate > 0.3:
            recommendations.append("Practice more before moving to advanced topics")
        
        return recommendations
    
    def _calculate_optimal_session_length(self, sessions: List[LearningSession]) -> int:
        """Calculate optimal session length based on historical data"""
        if not sessions:
            return 30  # Default 30 minutes
        
        # Find sessions with high engagement and performance
        optimal_sessions = [s for s in sessions 
                          if s.engagement_score > 0.7 and s.performance_score > 0.7 and s.end_time]
        
        if not optimal_sessions:
            return 30
        
        durations = [(s.end_time - s.start_time).total_seconds() / 60 for s in optimal_sessions]
        return int(sum(durations) / len(durations))
    
    def _analyze_learning_patterns(self, sessions: List[LearningSession]) -> Dict:
        """Analyze learning patterns from session data"""
        if not sessions:
            return {}
        
        patterns = {
            'peak_performance_times': [],
            'learning_velocity_trend': 'stable',
            'common_struggle_areas': [],
            'strength_areas': []
        }
        
        # Analyze performance by time of day
        time_performance = {}
        for session in sessions:
            hour = session.start_time.hour
            if hour not in time_performance:
                time_performance[hour] = []
            time_performance[hour].append(session.performance_score)
        
        # Find peak performance times
        for hour, scores in time_performance.items():
            avg_score = sum(scores) / len(scores)
            if avg_score > 0.8:
                patterns['peak_performance_times'].append(f"{hour}:00")
        
        return patterns
    
    def _track_learning_progress(self, sessions: List[LearningSession]) -> Dict:
        """Track learning progress over time"""
        if not sessions:
            return {}
        
        # Sort sessions by date
        sorted_sessions = sorted(sessions, key=lambda s: s.start_time)
        
        progress = {
            'performance_trend': [],
            'engagement_trend': [],
            'completion_trend': [],
            'overall_progress': 'improving'  # or 'declining', 'stable'
        }
        
        # Calculate trends
        for session in sorted_sessions:
            progress['performance_trend'].append({
                'date': session.start_time.isoformat(),
                'score': session.performance_score
            })
            progress['engagement_trend'].append({
                'date': session.start_time.isoformat(),
                'score': session.engagement_score
            })
            progress['completion_trend'].append({
                'date': session.start_time.isoformat(),
                'rate': session.completion_rate
            })
        
        # Determine overall progress direction
        if len(sorted_sessions) >= 5:
            recent_performance = sum(s.performance_score for s in sorted_sessions[-3:]) / 3
            earlier_performance = sum(s.performance_score for s in sorted_sessions[:3]) / 3
            
            if recent_performance > earlier_performance + 0.1:
                progress['overall_progress'] = 'improving'
            elif recent_performance < earlier_performance - 0.1:
                progress['overall_progress'] = 'declining'
            else:
                progress['overall_progress'] = 'stable'
        
        return progress

# Example usage:
# platform = PersonalizedLearningPlatform()
# 
# # Register a student
# student_profile = StudentProfile(
#     student_id="student_001",
#     name="Alice Johnson",
#     age=15,
#     grade_level="10th",
#     learning_style=LearningStyle.VISUAL,
#     interests=["science", "art", "technology"],
#     learning_pace=1.2,
#     attention_span=45
# )
# 
# registration = platform.register_student(student_profile)
# print("Student registered:", registration)
# 
# # Create learning objective
# objective = LearningObjective(
#     objective_id="obj_001",
#     title="Introduction to Python Programming",
#     description="Learn basic Python syntax and concepts",
#     subject="Computer Science",
#     difficulty_level=DifficultyLevel.BEGINNER,
#     estimated_duration=60,
#     skills_gained=["python_basics", "programming_logic"]
# )
# 
# platform.learning_objectives[objective.objective_id] = objective
# 
# # Start learning session
# session_config = platform.start_learning_session("student_001", "obj_001")
# print("Learning session started:", session_config)
# 
# # Generate analytics
# analytics = platform.generate_learning_analytics("student_001")
# print("Learning Analytics:", analytics)

class BlockchainCredentialSystem {
  constructor(networkConfig) {
    this.networkConfig = networkConfig;
    this.credentials = new Map();
    this.institutions = new Map();
    this.verificationRequests = [];
    this.digitalBadges = new Map();
    this.skillStandards = this.initializeSkillStandards();
  }

  initializeSkillStandards() {
    return {
      'technical_skills': {
        'programming': {
          levels: ['beginner', 'intermediate', 'advanced', 'expert'],
          verification_criteria: {
            'beginner': { min_projects: 1, min_score: 70 },
            'intermediate': { min_projects: 3, min_score: 80 },
            'advanced': { min_projects: 5, min_score: 85 },
            'expert': { min_projects: 10, min_score: 90 }
          }
        },
        'data_science': {
          levels: ['foundation', 'practitioner', 'specialist', 'expert'],
          verification_criteria: {
            'foundation': { min_courses: 2, min_score: 75 },
            'practitioner': { min_courses: 4, min_score: 80 },
            'specialist': { min_courses: 6, min_score: 85 },
            'expert': { min_courses: 8, min_score: 90 }
          }
        }
      },
      'soft_skills': {
        'leadership': {
          levels: ['emerging', 'developing', 'proficient', 'advanced'],
          verification_criteria: {
            'emerging': { peer_evaluations: 3, min_score: 70 },
            'developing': { peer_evaluations: 5, min_score: 75 },
            'proficient': { peer_evaluations: 8, min_score: 80 },
            'advanced': { peer_evaluations: 12, min_score: 85 }
          }
        }
      }
    };
  }

  // Register educational institution
  async registerInstitution(institutionData) {
    const institution = {
      id: institutionData.id,
      name: institutionData.name,
      type: institutionData.type, // university, certification_body, online_platform
      accreditation: institutionData.accreditation,
      public_key: institutionData.public_key,
      verification_status: 'pending',
      registered_at: new Date(),
      credentials_issued: 0,
      reputation_score: 0
    };

    // Verify institution credentials
    const verification = await this.verifyInstitution(institution);
    institution.verification_status = verification.status;
    institution.reputation_score = verification.initial_score;

    this.institutions.set(institution.id, institution);

    // Record on blockchain
    const blockchainTx = await this.recordOnBlockchain({
      type: 'institution_registration',
      data: {
        institution_id: institution.id,
        name: institution.name,
        public_key: institution.public_key,
        verification_status: institution.verification_status
      },
      timestamp: new Date().toISOString()
    });

    return {
      institution_id: institution.id,
      verification_status: institution.verification_status,
      blockchain_tx: blockchainTx.hash,
      reputation_score: institution.reputation_score
    };
  }

  // Issue digital credential
  async issueCredential(credentialData) {
    const institution = this.institutions.get(credentialData.issuer_id);
    if (!institution || institution.verification_status !== 'verified') {
      throw new Error('Institution not verified or not found');
    }

    // Generate unique credential ID
    const credentialId = this.generateCredentialId();
    
    const credential = {
      id: credentialId,
      recipient: {
        id: credentialData.recipient_id,
        name: credentialData.recipient_name,
        email: credentialData.recipient_email
      },
      issuer: {
        id: credentialData.issuer_id,
        name: institution.name,
        public_key: institution.public_key
      },
      credential_details: {
        title: credentialData.title,
        description: credentialData.description,
        credential_type: credentialData.type, // degree, certificate, badge, micro_credential
        subject_area: credentialData.subject_area,
        level: credentialData.level,
        grade: credentialData.grade,
        completion_date: credentialData.completion_date,
        expiration_date: credentialData.expiration_date
      },
      verification_data: {
        assessment_scores: credentialData.assessment_scores || [],
        learning_outcomes: credentialData.learning_outcomes || [],
        competencies_demonstrated: credentialData.competencies || [],
        evidence_links: credentialData.evidence_links || []
      },
      blockchain_proof: null,
      issued_at: new Date(),
      status: 'active'
    };

    // Generate digital signature
    credential.digital_signature = await this.generateDigitalSignature(
      credential,
      institution.private_key
    );

    // Create blockchain record
    const blockchainRecord = await this.recordOnBlockchain({
      type: 'credential_issuance',
      data: {
        credential_id: credentialId,
        recipient_id: credential.recipient.id,
        issuer_id: credential.issuer.id,
        credential_hash: this.generateCredentialHash(credential),
        issued_at: credential.issued_at
      },
      timestamp: new Date().toISOString()
    });

    credential.blockchain_proof = {
      transaction_hash: blockchainRecord.hash,
      block_number: blockchainRecord.blockNumber,
      network: this.networkConfig.network_name
    };

    // Store credential
    this.credentials.set(credentialId, credential);
    
    // Update institution statistics
    institution.credentials_issued += 1;
    
    // Check for badge eligibility
    const badges = await this.checkBadgeEligibility(credential);
    
    return {
      credential_id: credentialId,
      blockchain_proof: credential.blockchain_proof,
      verification_url: `${this.networkConfig.base_url}/verify/${credentialId}`,
      digital_signature: credential.digital_signature,
      eligible_badges: badges
    };
  }

  // Issue digital badge for specific achievements
  async issueDigitalBadge(badgeData) {
    const badgeId = this.generateBadgeId();
    
    const badge = {
      id: badgeId,
      recipient: {
        id: badgeData.recipient_id,
        name: badgeData.recipient_name
      },
      issuer: {
        id: badgeData.issuer_id,
        name: this.institutions.get(badgeData.issuer_id)?.name
      },
      badge_details: {
        name: badgeData.badge_name,
        description: badgeData.description,
        criteria: badgeData.criteria,
        skill_category: badgeData.skill_category,
        skill_level: badgeData.skill_level,
        icon_url: badgeData.icon_url,
        color_scheme: badgeData.color_scheme
      },
      evidence: {
        supporting_credentials: badgeData.supporting_credentials || [],
        project_links: badgeData.project_links || [],
        peer_endorsements: badgeData.peer_endorsements || [],
        assessment_results: badgeData.assessment_results || []
      },
      issued_at: new Date(),
      expires_at: badgeData.expires_at,
      status: 'active'
    };

    // Verify badge criteria are met
    const criteriaVerification = await this.verifyBadgeCriteria(badge);
    if (!criteriaVerification.verified) {
      throw new Error(`Badge criteria not met: ${criteriaVerification.reason}`);
    }

    // Record on blockchain
    const blockchainRecord = await this.recordOnBlockchain({
      type: 'badge_issuance',
      data: {
        badge_id: badgeId,
        recipient_id: badge.recipient.id,
        issuer_id: badge.issuer.id,
        badge_hash: this.generateBadgeHash(badge),
        criteria_verification: criteriaVerification
      },
      timestamp: new Date().toISOString()
    });

    badge.blockchain_proof = {
      transaction_hash: blockchainRecord.hash,
      block_number: blockchainRecord.blockNumber,
      verification_score: criteriaVerification.score
    };

    this.digitalBadges.set(badgeId, badge);

    return {
      badge_id: badgeId,
      blockchain_proof: badge.blockchain_proof,
      verification_url: `${this.networkConfig.base_url}/verify/badge/${badgeId}`,
      display_data: {
        name: badge.badge_details.name,
        icon_url: badge.badge_details.icon_url,
        issued_date: badge.issued_at,
        skill_level: badge.badge_details.skill_level
      }
    };
  }

  // Verify credential authenticity
  async verifyCredential(credentialId, verificationRequest = {}) {
    const credential = this.credentials.get(credentialId);
    if (!credential) {
      return {
        verified: false,
        error: 'Credential not found',
        status: 'not_found'
      };
    }

    const verification = {
      credential_id: credentialId,
      verified: false,
      verification_timestamp: new Date(),
      checks_performed: [],
      verification_score: 0,
      details: {}
    };

    // Check blockchain integrity
    const blockchainVerification = await this.verifyBlockchainIntegrity(
      credential.blockchain_proof
    );
    verification.checks_performed.push('blockchain_integrity');
    verification.details.blockchain_verification = blockchainVerification;
    
    if (blockchainVerification.valid) {
      verification.verification_score += 30;
    }

    // Verify digital signature
    const signatureVerification = await this.verifyDigitalSignature(
      credential,
      credential.digital_signature
    );
    verification.checks_performed.push('digital_signature');
    verification.details.signature_verification = signatureVerification;
    
    if (signatureVerification.valid) {
      verification.verification_score += 25;
    }

    // Check issuer validity
    const issuer = this.institutions.get(credential.issuer.id);
    const issuerVerification = {
      valid: issuer && issuer.verification_status === 'verified',
      issuer_reputation: issuer?.reputation_score || 0,
      issuer_name: issuer?.name || 'Unknown'
    };
    verification.checks_performed.push('issuer_validity');
    verification.details.issuer_verification = issuerVerification;
    
    if (issuerVerification.valid) {
      verification.verification_score += 20;
    }

    // Check expiration status
    const isExpired = credential.credential_details.expiration_date && 
                     new Date(credential.credential_details.expiration_date) < new Date();
    verification.checks_performed.push('expiration_status');
    verification.details.expiration_check = {
      expired: isExpired,
      expiration_date: credential.credential_details.expiration_date
    };
    
    if (!isExpired) {
      verification.verification_score += 15;
    }

    // Check for revocation
    const revocationCheck = await this.checkRevocationStatus(credentialId);
    verification.checks_performed.push('revocation_status');
    verification.details.revocation_check = revocationCheck;
    
    if (!revocationCheck.revoked) {
      verification.verification_score += 10;
    }

    // Determine overall verification status
    verification.verified = verification.verification_score >= 80;
    verification.confidence_level = this.calculateConfidenceLevel(verification.verification_score);

    // Log verification request
    this.verificationRequests.push({
      credential_id: credentialId,
      requester: verificationRequest.requester_id || 'anonymous',
      timestamp: new Date(),
      result: verification.verified,
      score: verification.verification_score
    });

    return verification;
  }

  // Create shareable credential portfolio
  async createCredentialPortfolio(recipientId, options = {}) {
    const recipientCredentials = Array.from(this.credentials.values())
      .filter(cred => cred.recipient.id === recipientId && cred.status === 'active');
    
    const recipientBadges = Array.from(this.digitalBadges.values())
      .filter(badge => badge.recipient.id === recipientId && badge.status === 'active');

    // Group credentials by type and subject
    const groupedCredentials = this.groupCredentials(recipientCredentials);
    const skillsMap = this.generateSkillsMap(recipientCredentials, recipientBadges);
    
    const portfolio = {
      recipient_id: recipientId,
      created_at: new Date(),
      last_updated: new Date(),
      portfolio_url: `${this.networkConfig.base_url}/portfolio/${recipientId}`,
      summary: {
        total_credentials: recipientCredentials.length,
        total_badges: recipientBadges.length,
        subjects_covered: [...new Set(recipientCredentials.map(c => c.credential_details.subject_area))],
        highest_level: this.determineHighestLevel(recipientCredentials),
        verification_score: this.calculatePortfolioVerificationScore(recipientCredentials)
      },
      credentials: groupedCredentials,
      digital_badges: this.groupBadges(recipientBadges),
      skills_profile: skillsMap,
      career_pathways: this.suggestCareerPathways(skillsMap),
      sharing_options: {
        public_url: options.public_access ? `${this.networkConfig.base_url}/public/${recipientId}` : null,
        employer_verification_code: this.generateVerificationCode(),
        social_media_ready: true,
        pdf_export_available: true
      }
    };

    return portfolio;
  }

  // Analytics and reporting
  generateSystemAnalytics() {
    const now = new Date();
    const last30Days = new Date(now.getTime() - 30 * 24 * 60 * 60 * 1000);
    
    const recentCredentials = Array.from(this.credentials.values())
      .filter(cred => cred.issued_at >= last30Days);
    
    const recentVerifications = this.verificationRequests
      .filter(req => req.timestamp >= last30Days);

    return {
      system_overview: {
        total_institutions: this.institutions.size,
        verified_institutions: Array.from(this.institutions.values())
          .filter(inst => inst.verification_status === 'verified').length,
        total_credentials: this.credentials.size,
        total_badges: this.digitalBadges.size,
        active_credentials: Array.from(this.credentials.values())
          .filter(cred => cred.status === 'active').length
      },
      recent_activity: {
        credentials_issued_30d: recentCredentials.length,
        verifications_performed_30d: recentVerifications.length,
        verification_success_rate: recentVerifications.length > 0 ? 
          recentVerifications.filter(req => req.result).length / recentVerifications.length : 0,
        average_verification_score: recentVerifications.length > 0 ?
          recentVerifications.reduce((sum, req) => sum + req.score, 0) / recentVerifications.length : 0
      },
      popular_subjects: this.getPopularSubjects(),
      institution_rankings: this.getInstitutionRankings(),
      credential_trends: this.analyzeCredentialTrends(recentCredentials)
    };
  }

  // Helper methods
  generateCredentialId() {
    return `cred_${Date.now()}_${Math.random().toString(36).substring(2, 15)}`;
  }

  generateBadgeId() {
    return `badge_${Date.now()}_${Math.random().toString(36).substring(2, 15)}`;
  }

  generateCredentialHash(credential) {
    // Simulate cryptographic hash generation
    const content = JSON.stringify({
      recipient: credential.recipient,
      issuer: credential.issuer.id,
      details: credential.credential_details,
      issued_at: credential.issued_at
    });
    return `hash_${Buffer.from(content).toString('base64').substring(0, 32)}`;
  }

  generateBadgeHash(badge) {
    const content = JSON.stringify({
      recipient: badge.recipient,
      issuer: badge.issuer.id,
      badge_details: badge.badge_details,
      issued_at: badge.issued_at
    });
    return `badge_hash_${Buffer.from(content).toString('base64').substring(0, 32)}`;
  }

  async recordOnBlockchain(data) {
    // Simulate blockchain transaction
    return {
      hash: `0x${Math.random().toString(16).substring(2, 66)}`,
      blockNumber: Math.floor(Math.random() * 1000000),
      timestamp: new Date(),
      gasUsed: 21000,
      status: 'confirmed'
    };
  }

  async verifyInstitution(institution) {
    // Simulate institution verification process
    const verificationFactors = {
      accreditation_valid: Math.random() > 0.1, // 90% chance
      domain_verified: Math.random() > 0.05,    // 95% chance
      reputation_check: Math.random() > 0.2     // 80% chance
    };
    
    const score = Object.values(verificationFactors).filter(Boolean).length * 25;
    
    return {
      status: score >= 75 ? 'verified' : 'pending',
      initial_score: score,
      verification_factors: verificationFactors
    };
  }

  calculateConfidenceLevel(score) {
    if (score >= 90) return 'very_high';
    if (score >= 80) return 'high';
    if (score >= 70) return 'medium';
    if (score >= 60) return 'low';
    return 'very_low';
  }
}

// Example usage:
// const credentialSystem = new BlockchainCredentialSystem({
//   network_name: 'EduChain',
//   base_url: 'https://credentials.edu',
//   contract_address: '0x123...'
// });
// 
// // Register institution
// await credentialSystem.registerInstitution({
//   id: 'mit_001',
//   name: 'Massachusetts Institute of Technology',
//   type: 'university',
//   accreditation: 'NECHE',
//   public_key: '0xabc123...'
// });
// 
// // Issue credential
// await credentialSystem.issueCredential({
//   recipient_id: 'student_123',
//   recipient_name: 'John Doe',
//   recipient_email: 'john@example.com',
//   issuer_id: 'mit_001',
//   title: 'Bachelor of Science in Computer Science',
//   type: 'degree',
//   subject_area: 'Computer Science',
//   level: 'undergraduate',
//   grade: 'A',
//   completion_date: '2025-05-15'
// });