integrations/atom_education_customization_service.py

"""
ATOM Education Industry Customization Service
FERPA compliant educational AI and student management system
"""

import asyncio
import base64
from collections import Counter, defaultdict
from dataclasses import asdict, dataclass
from datetime import datetime, timedelta, timezone
from enum import Enum
import hashlib
import hmac
import json
import logging
import os
import time
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from urllib.parse import urlencode
import aiohttp
import httpx
import numpy as np
import pandas as pd
from pydantic import BaseModel, Field
from core.circuit_breaker import circuit_breaker
from core.rate_limiter import rate_limiter, should_retry, calculate_backoff
from core.audit_logger import log_integration_call, log_integration_error, log_integration_attempt, log_integration_complete
from fastapi import HTTPException


# Import existing ATOM services
try:
    from ai_enhanced_service import (
        AIModelType,
        AIRequest,
        AIResponse,
        AIServiceType,
        AITaskType,
        ai_enhanced_service,
    )
    from atom_ai_integration import atom_ai_integration
    from atom_discord_integration import atom_discord_integration
    from atom_enterprise_security_service import (
        ComplianceStandard,
        SecurityLevel,
        atom_enterprise_security_service,
    )
    from atom_google_chat_integration import atom_google_chat_integration
    from atom_hubspot_integration_service import atom_hubspot_integration_service
    from atom_quickbooks_integration_service import atom_quickbooks_integration_service
    from atom_slack_integration import atom_slack_integration
    from atom_teams_integration import atom_teams_integration
    from atom_telegram_integration import atom_telegram_integration
    from atom_video_ai_service import atom_video_ai_service
    from atom_voice_ai_service import atom_voice_ai_service
    from atom_voice_video_integration_service import atom_voice_video_integration_service
    from atom_whatsapp_integration import atom_whatsapp_integration
    from atom_workflow_automation_service import (
        AutomationPriority,
        AutomationStatus,
        atom_workflow_automation_service,
    )
    from atom_zendesk_integration_service import atom_zendesk_integration_service
    from atom_zoom_integration import atom_zoom_integration
except ImportError as e:
    logging.warning(f"Enterprise services not available: {e}")

# Configure logging
logger = logging.getLogger(__name__)

class EducationComplianceStandard(Enum):
    """Education compliance standards"""
    FERPA = "ferpa"
    COPPA = "coppa"
    IDEA = "idea"
    ADA = "ada"
    GDPR = "gdpr"
    CCPA = "ccpa"
    ISO_27001 = "iso_27001"
    NIST_800_53 = "nist_800_53"

class StudentStatus(Enum):
    """Student status"""
    ENROLLED = "enrolled"
    ACTIVE = "active"
    INACTIVE = "inactive"
    GRADUATED = "graduated"
    SUSPENDED = "suspended"
    WITHDRAWN = "withdrawn"
    TRANSFERRED = "transferred"
    ON_LEAVE = "on_leave"

class CourseType(Enum):
    """Course types"""
    REQUIRED = "required"
    ELECTIVE = "elective"
    CORE = "core"
    GENERAL_EDUCATION = "general_education"
    ADVANCED_PLACEMENT = "advanced_placement"
    HONORS = "honors"
    ONLINE = "online"
    HYBRID = "hybrid"
    LABORATORY = "laboratory"

class GradeLevel(Enum):
    """Grade levels"""
    KINDERGARTEN = "kindergarten"
    ELEMENTARY = "elementary"
    MIDDLE_SCHOOL = "middle_school"
    HIGH_SCHOOL = "high_school"
    UNDERGRADUATE = "undergraduate"
    GRADUATE = "graduate"
    POST_GRADUATE = "post_graduate"

class LearningAnalyticsType(Enum):
    """Learning analytics types"""
    STUDENT_PERFORMANCE = "student_performance"
    COURSE_EFFECTIVENESS = "course_effectiveness"
    TEACHER_PERFORMANCE = "teacher_performance"
    LEARNING_OUTCOMES = "learning_outcomes"
    ENGAGEMENT_METRICS = "engagement_metrics"
    DROPOUT_PREDICTION = "dropout_prediction"
    ATTENDANCE_ANALYTICS = "attendance_analytics"
    SKILL_ASSESSMENT = "skill_assessment"

@dataclass
class Student:
    """Student data model"""
    student_id: str
    student_number: str
    first_name: str
    last_name: str
    date_of_birth: datetime
    grade_level: GradeLevel
    gpa: float
    major: Optional[str]
    minor: Optional[str]
    email: str
    phone: str
    address: Dict[str, str]
    emergency_contacts: List[Dict[str, Any]]
    enrollment_date: datetime
    status: StudentStatus
    academic_standing: str
    credits_earned: float
    attendance_rate: float
    courses_enrolled: List[str]
    learning_disabilities: List[str]
    special_education_needs: List[str]
    parent_guardian_info: List[Dict[str, Any]]
    last_updated: datetime
    metadata: Dict[str, Any]

@dataclass
class Course:
    """Course data model"""
    course_id: str
    course_code: str
    title: str
    description: str
    instructor_id: str
    department: str
    course_type: CourseType
    credits: float
    max_capacity: int
    current_enrollment: int
    start_date: datetime
    end_date: datetime
    schedule: Dict[str, Any]
    learning_objectives: List[str]
    required_materials: List[Dict[str, Any]]
    assessment_methods: List[str]
    difficulty_level: str
    prerequisites: List[str]
    created_at: datetime
    updated_at: datetime
    metadata: Dict[str, Any]

@dataclass
class Assignment:
    """Assignment data model"""
    assignment_id: str
    course_id: str
    title: str
    description: str
    assignment_type: str
    due_date: datetime
    points_possible: float
    learning_objectives: List[str]
    rubric: Dict[str, Any]
    submission_type: str
    allowed_late_submission: bool
    late_penalty: float
    created_at: datetime
    updated_at: datetime
    metadata: Dict[str, Any]

@dataclass
class Grade:
    """Grade data model"""
    grade_id: str
    student_id: str
    course_id: str
    assignment_id: str
    score: float
    max_score: float
    percentage: float
    letter_grade: str
    submission_date: datetime
    graded_date: datetime
    grader_id: str
    feedback: str
    learning_mastery: Dict[str, float]
    created_at: datetime
    updated_at: datetime
    metadata: Dict[str, Any]

@dataclass
class LearningAnalytics:
    """Learning analytics data model"""
    analytics_id: str
    analytics_type: LearningAnalyticsType
    time_period: str
    start_date: datetime
    end_date: datetime
    student_id: Optional[str]
    course_id: Optional[str]
    instructor_id: Optional[str]
    metrics: Dict[str, Any]
    insights: List[str]
    recommendations: List[str]
    created_at: datetime
    metadata: Dict[str, Any]

class AtomEducationCustomizationService:
    """Advanced Education Industry Customization Service"""
    
    def __init__(self, tenant_id: str = "default", config: Dict[str, Any] = None):
        if config is None:
            config = {}
        self.config = config
        self.db = config.get('database')
        self.cache = config.get('cache')
        
        # Education API configuration
        self.education_config = {
            'ferpa_compliance': config.get('ferpa_compliance', True),
            'coppa_compliance': config.get('coppa_compliance', True),
            'idea_compliance': config.get('idea_compliance', True),
            'ada_compliance': config.get('ada_compliance', True),
            'gdpr_compliance': config.get('gdpr_compliance', True),
            'encryption_at_rest': config.get('encryption_at_rest', True),
            'encryption_in_transit': config.get('encryption_in_transit', True),
            'audit_logging': config.get('audit_logging', True),
            'access_control': config.get('access_control', True),
            'data_masking': config.get('data_masking', True),
            'retention_policy': config.get('retention_policy', '10_years'),
            'parent_portal_access': config.get('parent_portal_access', True),
            'educational_ai_enabled': config.get('educational_ai_enabled', True),
            'learning_analytics': config.get('learning_analytics', True),
            'personalized_learning': config.get('personalized_learning', True),
            'automated_grading': config.get('automated_grading', True),
            'plagiarism_detection': config.get('plagiarism_detection', True),
            'attendance_tracking': config.get('attendance_tracking', True),
            'student_performance_prediction': config.get('student_performance_prediction', True),
            'lms_integration': config.get('lms_integration', True),
            'student_information_system': config.get('student_information_system', True),
            'library_integration': config.get('library_integration', True),
            'library_integration': config.get('library_integration', True)
        }
        
        # API endpoints
        self.api_endpoints = {
            'students': '/api/v1/students',
            'courses': '/api/v1/courses',
            'instructors': '/api/v1/instructors',
            'assignments': '/api/v1/assignments',
            'grades': '/api/v1/grades',
            'attendance': '/api/v1/attendance',
            'learning_analytics': '/api/v1/learning_analytics',
            'enrollments': '/api/v1/enrollments',
            'compliance': '/api/v1/compliance'
        }
        
        # Integration state
        self.is_initialized = False
        self.compliance_standards: List[EducationComplianceStandard] = []
        self.encryption_keys: Dict[str, str] = {}
        self.access_policies: Dict[str, Dict[str, Any]] = {}
        self.audit_logs: List[Dict[str, Any]] = []
        self.student_workflows: Dict[str, Dict[str, Any]] = {}
        self.learning_pathways: Dict[str, Dict[str, Any]] = {}
        self.assessment_rubrics: Dict[str, Dict[str, Any]] = {}
        
        # LMS integration
        self.lms_integration = None
        if self.education_config['lms_integration']:
            self.lms_integration = self._initialize_lms_integration()
        
        # Student Information System integration
        self.sis_integration = None
        if self.education_config['student_information_system']:
            self.sis_integration = self._initialize_sis_integration()
        
        # Enterprise integration (use safe defaults if optional services didn't import)
        self.enterprise_security = config.get('security_service') or globals().get('atom_enterprise_security_service')
        self.enterprise_automation = config.get('automation_service') or globals().get('atom_workflow_automation_service')
        self.ai_service = config.get('ai_service') or globals().get('ai_enhanced_service')
        
        # Platform integrations (use safe defaults if optional services didn't import)
        self.platform_integrations = {}
        _slack = globals().get('atom_slack_integration')
        if _slack:
            self.platform_integrations['slack'] = _slack
        _teams = globals().get('atom_teams_integration')
        if _teams:
            self.platform_integrations['teams'] = _teams
        _google_chat = globals().get('atom_google_chat_integration')
        if _google_chat:
            self.platform_integrations['google_chat'] = _google_chat
        _discord = globals().get('atom_discord_integration')
        if _discord:
            self.platform_integrations['discord'] = _discord
        _telegram = globals().get('atom_telegram_integration')
        if _telegram:
            self.platform_integrations['telegram'] = _telegram
        _whatsapp = globals().get('atom_whatsapp_integration')
        if _whatsapp:
            self.platform_integrations['whatsapp'] = _whatsapp
        _zoom = globals().get('atom_zoom_integration')
        if _zoom:
            self.platform_integrations['zoom'] = _zoom
        
        # Analytics and monitoring
        self.analytics_metrics = {
            'total_students': 0,
            'active_students': 0,
            'total_courses': 0,
            'total_instructors': 0,
            'total_assignments': 0,
            'total_grades': 0,
            'average_gpa': 0.0,
            'average_attendance': 0.0,
            'course_completion_rate': 0.0,
            'student_satisfaction': 0.0,
            'learning_outcomes_achievement': 0.0,
            'engagement_score': 0.0,
            'dropout_rate': 0.0,
            'graduation_rate': 0.0,
            'compliance_score': 0.0,
            'educational_ai_accuracy': 0.0,
            'personalized_learning_effectiveness': 0.0,
            'automated_grading_accuracy': 0.0,
            'plagiarism_detection_accuracy': 0.0,
            'grade_level_distribution': defaultdict(int),
            'course_difficulty_distribution': defaultdict(int),
            'department_performance': defaultdict(dict),
            'instructor_performance': defaultdict(dict),
            'student_performance': defaultdict(list),
            'learning_objectives_mastery': defaultdict(float)
        }
        
        # Performance metrics
        self.performance_metrics = {
            'api_response_time': 0.0,
            'educational_ai_processing_time': 0.0,
            'compliance_check_time': 0.0,
            'encryption_processing_time': 0.0,
            'audit_log_processing_time': 0.0,
            'student_data_sync_time': 0.0,
            'lms_sync_time': 0.0,
            'analytics_generation_time': 0.0
        }
        
        logger.info("Education Customization Service initialized")
    
    async def initialize(self) -> bool:
        """Initialize Education Customization Service"""
        try:
            # Setup FERPA compliance
            await self._setup_ferpa_compliance()
            
            # Initialize LMS integration
            if self.lms_integration:
                await self._initialize_lms_connection()
            
            # Initialize SIS integration
            if self.sis_integration:
                await self._initialize_sis_connection()
            
            # Setup encryption and security
            await self._setup_encryption_and_security()
            
            # Setup audit logging
            await self._setup_audit_logging()
            
            # Setup access control
            await self._setup_access_control()
            
            # Setup educational AI features
            if self.education_config['educational_ai_enabled']:
                await self._setup_educational_ai()
            
            # Setup learning pathways
            if self.education_config['personalized_learning']:
                await self._setup_learning_pathways()
            
            # Setup automated grading
            if self.education_config['automated_grading']:
                await self._setup_automated_grading()
            
            # Setup plagiarism detection
            if self.education_config['plagiarism_detection']:
                await self._setup_plagiarism_detection()
            
            # Setup integrations
            await self._setup_integrations()
            
            # Load existing data
            await self._load_existing_data()
            
            # Start monitoring
            await self._start_monitoring()
            
            self.is_initialized = True
            logger.info("Education Customization Service initialized successfully")
            return True
            
        except Exception as e:
            logger.error(f"Error initializing Education Customization Service: {e}")
            return False
    
    async def create_student(self, student_data: Dict[str, Any], platform: str = None) -> Dict[str, Any]:
        """Create new student with FERPA compliance"""
        # Start audit logging
        audit_ctx = log_integration_attempt("atom_education_customization", "initialize", locals())
        try:
            # Check circuit breaker
            if not await circuit_breaker.is_enabled("atom_education_customization"):
                logger.warning(f"Circuit breaker is open for atom_education_customization")
                log_integration_complete(audit_ctx, error=Exception("Circuit breaker open"))
                raise HTTPException(
                    status_code=503,
                    detail=f"Atom_education_customization integration temporarily disabled"
                )

            # Check rate limiter
            is_limited, remaining = await rate_limiter.is_rate_limited("atom_education_customization")
            if is_limited:
                logger.warning(f"Rate limit exceeded for atom_education_customization")
                log_integration_complete(audit_ctx, error=Exception("Rate limit exceeded"))
                raise HTTPException(
                    status_code=429,
                    detail=f"Rate limit exceeded for atom_education_customization"
                )

            start_time = time.time()
            # Update analytics
            self.analytics_metrics['total_students'] += 1
            self.analytics_metrics['active_students'] += 1
            self.analytics_metrics['grade_level_distribution'][student_data.get('grade_level', 'undergraduate').value] += 1
            # FERPA compliance check
            if self.education_config['ferpa_compliance']:
                compliance_check = await self._perform_ferpa_compliance_check(student_data)
                if not compliance_check['passed']:
                    return {'success': False, 'error': compliance_check['reason']}
            # Educational AI analysis for learning assessment
            if self.education_config['educational_ai_enabled']:
                ai_analysis = await self._analyze_student_with_educational_ai(student_data)
                student_data.update(ai_analysis)
            # Encrypt sensitive data
            encrypted_data = await self._encrypt_student_data(student_data)
            # Prepare student payload
            student_payload = {
                'student_id': encrypted_data['student_id'],
                'student_number': encrypted_data['student_number'],
                'first_name': encrypted_data['first_name'],
                'last_name': encrypted_data['last_name'],
                'date_of_birth': encrypted_data['date_of_birth'].isoformat(),
                'grade_level': encrypted_data['grade_level'].value,
                'gpa': encrypted_data['gpa'],
                'major': encrypted_data['major'],
                'minor': encrypted_data['minor'],
                'email': encrypted_data['email'],
                'phone': encrypted_data['phone'],
                'address': encrypted_data['address'],
                'emergency_contacts': encrypted_data['emergency_contacts'],
                'enrollment_date': encrypted_data['enrollment_date'].isoformat(),
                'status': encrypted_data.get('status', 'active'),
                'academic_standing': encrypted_data.get('academic_standing', 'good'),
                'credits_earned': encrypted_data['credits_earned'],
                'attendance_rate': encrypted_data['attendance_rate'],
                'courses_enrolled': encrypted_data['courses_enrolled'],
                'learning_disabilities': encrypted_data['learning_disabilities'],
                'special_education_needs': encrypted_data['special_education_needs'],
                'parent_guardian_info': encrypted_data['parent_guardian_info'],
                'last_updated': datetime.utcnow().isoformat(),
                'metadata': {
                    'created_by': 'atom_education_service',
                    'ferpa_compliant': True,
                    'encryption_enabled': True
                }
            }
            # Create student via API
            headers = await self._get_auth_headers()
            async with httpx.AsyncClient() as client:
                response = await client.post(
                    f"{self.config.get('base_url')}{self.api_endpoints['students']}",
                    headers=headers,
                    json=student_payload,
                    timeout=30.0
                )
                if response.status_code == 201:
                    student = response.json()
                    # Update performance metrics
                    creation_time = time.time() - start_time
                    self.performance_metrics['api_response_time'] = creation_time
                    # Log audit trail
                    await self._log_audit_event('student_created', student_data, encrypted_data)
                    # Sync with SIS
                    if self.sis_integration:
                        await self._sync_student_to_sis(student)
                    # Notify relevant platforms
                    if platform and platform in self.platform_integrations:
                        await self._notify_platform_student_created(student, platform)
                    # Trigger workflows
                    await self._trigger_student_workflows(student, 'created')
                    logger.info(f"Student created successfully: {student['student_id']}")
                    return {
                        'success': True,
                        'student': student,
                        'student_id': student['student_id'],
                        'creation_time': creation_time
                    }
                else:
                    error_msg = f"Failed to create student: {response.status_code} - {response.text}"
                    logger.error(error_msg)
                    return {'success': False, 'error': error_msg}
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            logger.error(f"Error creating student: {e}")
            return {'success': False, 'error': str(e)}
    
    async def create_course(self, course_data: Dict[str, Any], platform: str = None) -> Dict[str, Any]:
        """Create new course with FERPA compliance"""
        # Start audit logging
        audit_ctx = log_integration_attempt("atom_education_customization", "create_student", locals())
            # Check circuit breaker
            if not await circuit_breaker.is_enabled("atom_education_customization"):
                logger.warning(f"Circuit breaker is open for atom_education_customization")
                log_integration_complete(audit_ctx, error=Exception("Circuit breaker open"))
                raise HTTPException(
                    status_code=503,
                    detail=f"Atom_education_customization integration temporarily disabled"
                )
            # Check rate limiter
            is_limited, remaining = await rate_limiter.is_rate_limited("atom_education_customization")
            if is_limited:
                logger.warning(f"Rate limit exceeded for atom_education_customization")
                log_integration_complete(audit_ctx, error=Exception("Rate limit exceeded"))
                raise HTTPException(
                    status_code=429,
                    detail=f"Rate limit exceeded for atom_education_customization"
                )
        try:
            start_time = time.time()
            
            # Update analytics
            self.analytics_metrics['total_courses'] += 1
            self.analytics_metrics['course_difficulty_distribution'][course_data.get('difficulty_level', 'intermediate')] += 1
            
            # FERPA compliance check
            if self.education_config['ferpa_compliance']:
                compliance_check = await self._perform_ferpa_compliance_check(course_data)
                if not compliance_check['passed']:
                    return {'success': False, 'error': compliance_check['reason']}
            
            # Educational AI analysis for course optimization
            if self.education_config['educational_ai_enabled']:
                ai_analysis = await self._analyze_course_with_educational_ai(course_data)
                course_data.update(ai_analysis)
            
            # Prepare course payload
            course_payload = {
                'course_id': course_data['course_id'],
                'course_code': course_data['course_code'],
                'title': course_data['title'],
                'description': course_data['description'],
                'instructor_id': course_data['instructor_id'],
                'department': course_data['department'],
                'course_type': course_data['course_type'].value,
                'credits': course_data['credits'],
                'max_capacity': course_data['max_capacity'],
                'current_enrollment': course_data['current_enrollment'],
                'start_date': course_data['start_date'].isoformat(),
                'end_date': course_data['end_date'].isoformat(),
                'schedule': course_data['schedule'],
                'learning_objectives': course_data['learning_objectives'],
                'required_materials': course_data['required_materials'],
                'assessment_methods': course_data['assessment_methods'],
                'difficulty_level': course_data['difficulty_level'],
                'prerequisites': course_data['prerequisites'],
                'created_at': datetime.utcnow().isoformat(),
                'updated_at': datetime.utcnow().isoformat(),
                'metadata': {
                    'created_by': 'atom_education_service',
                    'ferpa_compliant': True,
                    'educational_ai_enabled': self.education_config['educational_ai_enabled']
                }
            }
            
            # Create course via API
            headers = await self._get_auth_headers()
            async with httpx.AsyncClient() as client:
                response = await client.post(
                    f"{self.config.get('base_url')}{self.api_endpoints['courses']}",
                    headers=headers,
                    json=course_payload,
                    timeout=30.0
                )
                
                if response.status_code == 201:
                    course = response.json()
                    
                    # Update performance metrics
                    creation_time = time.time() - start_time
                    self.performance_metrics['api_response_time'] = creation_time
                    
                    # Log audit trail
                    await self._log_audit_event('course_created', course_data, course_payload)
                    
                    # Sync with LMS
                    if self.lms_integration:
                        await self._sync_course_to_lms(course)
                    
                    # Notify relevant platforms
                    if platform and platform in self.platform_integrations:
                        await self._notify_platform_course_created(course, platform)
                    
                    # Trigger workflows
                    await self._trigger_course_workflows(course, 'created')
                    
                    logger.info(f"Course created successfully: {course['course_id']}")
                    return {
                        'success': True,
                        'course': course,
                        'course_id': course['course_id'],
                        'creation_time': creation_time
                    }
                else:
                    error_msg = f"Failed to create course: {response.status_code} - {response.text}"
                    logger.error(error_msg)
                    return {'success': False, 'error': error_msg}
                    
        except Exception as e:
            logger.error(f"Error creating course: {e}")
            return {'success': False, 'error': str(e)}
    
    async def create_assignment(self, assignment_data: Dict[str, Any], platform: str = None) -> Dict[str, Any]:
        """Create new assignment with FERPA compliance"""
        # Start audit logging
        audit_ctx = log_integration_attempt("atom_education_customization", "create_course", locals())
            # Check circuit breaker
            if not await circuit_breaker.is_enabled("atom_education_customization"):
                logger.warning(f"Circuit breaker is open for atom_education_customization")
                log_integration_complete(audit_ctx, error=Exception("Circuit breaker open"))
                raise HTTPException(
                    status_code=503,
                    detail=f"Atom_education_customization integration temporarily disabled"
                )
            # Check rate limiter
            is_limited, remaining = await rate_limiter.is_rate_limited("atom_education_customization")
            if is_limited:
                logger.warning(f"Rate limit exceeded for atom_education_customization")
                log_integration_complete(audit_ctx, error=Exception("Rate limit exceeded"))
                raise HTTPException(
                    status_code=429,
                    detail=f"Rate limit exceeded for atom_education_customization"
                )
        try:
            start_time = time.time()
            
            # Update analytics
            self.analytics_metrics['total_assignments'] += 1
            
            # FERPA compliance check
            if self.education_config['ferpa_compliance']:
                compliance_check = await self._perform_ferpa_compliance_check(assignment_data)
                if not compliance_check['passed']:
                    return {'success': False, 'error': compliance_check['reason']}
            
            # Educational AI analysis for assignment optimization
            if self.education_config['educational_ai_enabled']:
                ai_analysis = await self._analyze_assignment_with_educational_ai(assignment_data)
                assignment_data.update(ai_analysis)
            
            # Prepare assignment payload
            assignment_payload = {
                'assignment_id': assignment_data['assignment_id'],
                'course_id': assignment_data['course_id'],
                'title': assignment_data['title'],
                'description': assignment_data['description'],
                'assignment_type': assignment_data['assignment_type'],
                'due_date': assignment_data['due_date'].isoformat(),
                'points_possible': assignment_data['points_possible'],
                'learning_objectives': assignment_data['learning_objectives'],
                'rubric': assignment_data['rubric'],
                'submission_type': assignment_data['submission_type'],
                'allowed_late_submission': assignment_data['allowed_late_submission'],
                'late_penalty': assignment_data['late_penalty'],
                'created_at': datetime.utcnow().isoformat(),
                'updated_at': datetime.utcnow().isoformat(),
                'metadata': {
                    'created_by': 'atom_education_service',
                    'ferpa_compliant': True,
                    'educational_ai_enabled': self.education_config['educational_ai_enabled'],
                    'automated_grading_enabled': self.education_config['automated_grading'],
                    'plagiarism_detection_enabled': self.education_config['plagiarism_detection']
                }
            }
            
            # Create assignment via API
            headers = await self._get_auth_headers()
            async with httpx.AsyncClient() as client:
                response = await client.post(
                    f"{self.config.get('base_url')}{self.api_endpoints['assignments']}",
                    headers=headers,
                    json=assignment_payload,
                    timeout=30.0
                )
                
                if response.status_code == 201:
                    assignment = response.json()
                    
                    # Update performance metrics
                    creation_time = time.time() - start_time
                    self.performance_metrics['api_response_time'] = creation_time
                    
                    # Log audit trail
                    await self._log_audit_event('assignment_created', assignment_data, assignment_payload)
                    
                    # Sync with LMS
                    if self.lms_integration:
                        await self._sync_assignment_to_lms(assignment)
                    
                    # Notify relevant platforms
                    if platform and platform in self.platform_integrations:
                        await self._notify_platform_assignment_created(assignment, platform)
                    
                    # Trigger workflows
                    await self._trigger_assignment_workflows(assignment, 'created')
                    
                    logger.info(f"Assignment created successfully: {assignment['assignment_id']}")
                    return {
                        'success': True,
                        'assignment': assignment,
                        'assignment_id': assignment['assignment_id'],
                        'creation_time': creation_time
                    }
                else:
                    error_msg = f"Failed to create assignment: {response.status_code} - {response.text}"
                    logger.error(error_msg)
                    return {'success': False, 'error': error_msg}
                    
        except Exception as e:
            logger.error(f"Error creating assignment: {e}")
            return {'success': False, 'error': str(e)}
    
    async def generate_learning_analytics(self, analytics_type: LearningAnalyticsType,
        # Start audit logging
        audit_ctx = log_integration_attempt("atom_education_customization", "generate_learning_analytics", locals())
            # Check circuit breaker
            if not await circuit_breaker.is_enabled("atom_education_customization"):
                logger.warning(f"Circuit breaker is open for atom_education_customization")
                log_integration_complete(audit_ctx, error=Exception("Circuit breaker open"))
                raise HTTPException(
                    status_code=503,
                    detail=f"Atom_education_customization integration temporarily disabled"
                )
            # Check rate limiter
            is_limited, remaining = await rate_limiter.is_rate_limited("atom_education_customization")
            if is_limited:
                logger.warning(f"Rate limit exceeded for atom_education_customization")
                log_integration_complete(audit_ctx, error=Exception("Rate limit exceeded"))
                raise HTTPException(
                    status_code=429,
                    detail=f"Rate limit exceeded for atom_education_customization"
                )
                                         time_period: str = '7d', student_id: str = None,
                                         course_id: str = None, instructor_id: str = None) -> Dict[str, Any]:
        """Generate learning analytics with FERPA compliance"""
        # Start audit logging
        audit_ctx = log_integration_attempt("atom_education_customization", "create_assignment", locals())
            # Check circuit breaker
            if not await circuit_breaker.is_enabled("atom_education_customization"):
                logger.warning(f"Circuit breaker is open for atom_education_customization")
                log_integration_complete(audit_ctx, error=Exception("Circuit breaker open"))
                raise HTTPException(
                    status_code=503,
                    detail=f"Atom_education_customization integration temporarily disabled"
                )
            # Check rate limiter
            is_limited, remaining = await rate_limiter.is_rate_limited("atom_education_customization")
            if is_limited:
                logger.warning(f"Rate limit exceeded for atom_education_customization")
                log_integration_complete(audit_ctx, error=Exception("Rate limit exceeded"))
                raise HTTPException(
                    status_code=429,
                    detail=f"Rate limit exceeded for atom_education_customization"
                )
        try:
            start_time = time.time()
            
            # Calculate date range
            end_date = datetime.utcnow()
            start_date = end_date - timedelta(days=7)  # Default to 7 days
            
            # FERPA compliance check for analytics
            if self.education_config['ferpa_compliance']:
                compliance_check = await self._verify_analytics_compliance(analytics_type, student_id)
                if not compliance_check['passed']:
                    return {'success': False, 'error': compliance_check['reason']}
            
            # Generate analytics based on type
            if analytics_type == LearningAnalyticsType.STUDENT_PERFORMANCE:
                analytics_data = await self._generate_student_performance_analytics(start_date, end_date, student_id, course_id)
            elif analytics_type == LearningAnalyticsType.COURSE_EFFECTIVENESS:
                analytics_data = await self._generate_course_effectiveness_analytics(start_date, end_date, course_id, instructor_id)
            elif analytics_type == LearningAnalyticsType.TEACHER_PERFORMANCE:
                analytics_data = await self._generate_teacher_performance_analytics(start_date, end_date, instructor_id, course_id)
            elif analytics_type == LearningAnalyticsType.LEARNING_OUTCOMES:
                analytics_data = await self._generate_learning_outcomes_analytics(start_date, end_date, course_id, student_id)
            elif analytics_type == LearningAnalyticsType.ENGAGEMENT_METRICS:
                analytics_data = await self._generate_engagement_metrics_analytics(start_date, end_date, student_id, course_id)
            elif analytics_type == LearningAnalyticsType.DROPOUT_PREDICTION:
                analytics_data = await self._generate_dropout_prediction_analytics(start_date, end_date, student_id)
            elif analytics_type == LearningAnalyticsType.ATTENDANCE_ANALYTICS:
                analytics_data = await self._generate_attendance_analytics(start_date, end_date, student_id, course_id)
            elif analytics_type == LearningAnalyticsType.SKILL_ASSESSMENT:
                analytics_data = await self._generate_skill_assessment_analytics(start_date, end_date, student_id, course_id)
            else:
                analytics_data = {'error': 'Unsupported analytics type'}
            
            # Add educational AI-powered insights
            if self.education_config['educational_ai_enabled']:
                insights = await self._generate_educational_ai_insights(analytics_data, analytics_type)
                analytics_data['ai_insights'] = insights
            
            # Create analytics object
            analytics = LearningAnalytics(
                analytics_id=f"analytics_{int(time.time())}",
                analytics_type=analytics_type,
                time_period=time_period,
                start_date=start_date,
                end_date=end_date,
                student_id=student_id,
                course_id=course_id,
                instructor_id=instructor_id,
                metrics=analytics_data,
                insights=analytics_data.get('insights', []),
                recommendations=analytics_data.get('recommendations', []),
                created_at=datetime.utcnow(),
                metadata={'generated_by': 'atom_education_service', 'ferpa_compliant': True}
            )
            
            # Update performance metrics
            generation_time = time.time() - start_time
            self.performance_metrics['analytics_generation_time'] = generation_time
            
            return {
                'success': True,
                'analytics': asdict(analytics),
                'generation_time': generation_time
            }
            
        except Exception as e:
            logger.error(f"Error generating learning analytics: {e}")
            return {'success': False, 'error': str(e)}
    
    async def _analyze_student_with_educational_ai(self, student_data: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze student data with educational AI"""
            start_time = time.time()
            # Prepare AI request for student analysis
            ai_request = AIRequest(
                request_id=f"student_analysis_{int(time.time())}",
                task_type=AITaskType.PREDICTION,
                model_type=AIModelType.GPT_4,
                service_type=AIServiceType.OPENAI,
                input_data={
                    'student_data': student_data,
                    'context': 'educational_student_analysis',
                    'analysis_types': [
                        'learning_style', 'academic_potential', 'at_risk_factors',
                        'personalized_learning_path', 'intervention_needs',
                        'subject_strengths', 'subject_weaknesses', 'motivation_factors'
                    ]
                },
                context={
                    'platform': 'education',
                    'task': 'student_analysis',
                    'ferpa_compliant': True
                },
                platform='education'
            )
            ai_response = await self.ai_service.process_ai_request(ai_request)
            if ai_response.ok and ai_response.output_data:
                analysis_result = ai_response.output_data
                educational_ai_suggestions = {
                    'learning_style': analysis_result.get('learning_style', 'visual'),
                    'academic_potential_score': analysis_result.get('academic_potential_score', 0.7),
                    'at_risk_factors': analysis_result.get('at_risk_factors', []),
                    'personalized_learning_path': analysis_result.get('personalized_learning_path', {}),
                    'intervention_needs': analysis_result.get('intervention_needs', []),
                    'subject_strengths': analysis_result.get('subject_strengths', []),
                    'subject_weaknesses': analysis_result.get('subject_weaknesses', []),
                    'motivation_factors': analysis_result.get('motivation_factors', []),
                    'learning_preferences': analysis_result.get('learning_preferences', {}),
                    'study_recommendations': analysis_result.get('study_recommendations', []),
                    'career_suggestions': analysis_result.get('career_suggestions', [])
                }
            else:
                educational_ai_suggestions = {
                    'learning_style': 'visual',
                    'academic_potential_score': 0.7,
                    'at_risk_factors': [],
                    'personalized_learning_path': {},
                    'intervention_needs': [],
                    'subject_strengths': [],
                    'subject_weaknesses': [],
                    'motivation_factors': [],
                    'learning_preferences': {},
                    'study_recommendations': [],
                    'career_suggestions': []
                }
            # Update performance metrics
            analysis_time = time.time() - start_time
            self.performance_metrics['educational_ai_processing_time'] = analysis_time
            # Update analytics
            self.analytics_metrics['educational_ai_accuracy'] = (
                (self.analytics_metrics['educational_ai_accuracy'] * 0.9 + 0.1)  # Simplified accuracy calculation
            )
            return educational_ai_suggestions
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error analyzing student with educational AI: {e}")
            return {
                'learning_style': 'visual',
                'academic_potential_score': 0.7,
                'at_risk_factors': [],
                'personalized_learning_path': {},
                'intervention_needs': [],
                'subject_strengths': [],
                'subject_weaknesses': [],
                'motivation_factors': [],
                'learning_preferences': {},
                'study_recommendations': [],
                'career_suggestions': []
            }
    
    async def _analyze_course_with_educational_ai(self, course_data: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze course data with educational AI"""
            start_time = time.time()
            # Prepare AI request for course analysis
            ai_request = AIRequest(
                request_id=f"course_analysis_{int(time.time())}",
                task_type=AITaskType.CONTENT_ANALYSIS,
                model_type=AIModelType.GPT_4,
                service_type=AIServiceType.OPENAI,
                input_data={
                    'course_data': course_data,
                    'context': 'educational_course_analysis',
                    'analysis_types': [
                        'course_optimization', 'content_difficulty', 'student_engagement',
                        'assessment_alignment', 'learning_objective_achievement',
                        'prerequisite_effectiveness', 'teaching_strategy_recommendations'
                    ]
                },
                context={
                    'platform': 'education',
                    'task': 'course_analysis',
                    'ferpa_compliant': True
                },
                platform='education'
            )
            ai_response = await self.ai_service.process_ai_request(ai_request)
            if ai_response.ok and ai_response.output_data:
                analysis_result = ai_response.output_data
                educational_ai_suggestions = {
                    'course_optimization_tips': analysis_result.get('course_optimization_tips', []),
                    'content_difficulty_score': analysis_result.get('content_difficulty_score', 0.5),
                    'predicted_student_engagement': analysis_result.get('predicted_student_engagement', 0.7),
                    'assessment_alignment_score': analysis_result.get('assessment_alignment_score', 0.8),
                    'learning_objective_achievement_score': analysis_result.get('learning_objective_achievement_score', 0.75),
                    'prerequisite_effectiveness': analysis_result.get('prerequisite_effectiveness', 0.8),
                    'teaching_strategy_recommendations': analysis_result.get('teaching_strategy_recommendations', []),
                    'content_recommendations': analysis_result.get('content_recommendations', []),
                    'technology_integration_suggestions': analysis_result.get('technology_integration_suggestions', []),
                    'inclusive_design_recommendations': analysis_result.get('inclusive_design_recommendations', [])
                }
            else:
                educational_ai_suggestions = {
                    'course_optimization_tips': [],
                    'content_difficulty_score': 0.5,
                    'predicted_student_engagement': 0.7,
                    'assessment_alignment_score': 0.8,
                    'learning_objective_achievement_score': 0.75,
                    'prerequisite_effectiveness': 0.8,
                    'teaching_strategy_recommendations': [],
                    'content_recommendations': [],
                    'technology_integration_suggestions': [],
                    'inclusive_design_recommendations': []
                }
            # Update performance metrics
            analysis_time = time.time() - start_time
            self.performance_metrics['educational_ai_processing_time'] = analysis_time
            return educational_ai_suggestions
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error analyzing course with educational AI: {e}")
            return {
                'course_optimization_tips': [],
                'content_difficulty_score': 0.5,
                'predicted_student_engagement': 0.7,
                'assessment_alignment_score': 0.8,
                'learning_objective_achievement_score': 0.75,
                'prerequisite_effectiveness': 0.8,
                'teaching_strategy_recommendations': [],
                'content_recommendations': [],
                'technology_integration_suggestions': [],
                'inclusive_design_recommendations': []
            }
    
    async def _analyze_assignment_with_educational_ai(self, assignment_data: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze assignment data with educational AI"""
            start_time = time.time()
            # Prepare AI request for assignment analysis
            ai_request = AIRequest(
                request_id=f"assignment_analysis_{int(time.time())}",
                task_type=AITaskType.CONTENT_ANALYSIS,
                model_type=AIModelType.GPT_4,
                service_type=AIServiceType.OPENAI,
                input_data={
                    'assignment_data': assignment_data,
                    'context': 'educational_assignment_analysis',
                    'analysis_types': [
                        'assignment_effectiveness', 'difficulty_level', 'time_estimation',
                        'learning_objective_alignment', 'assessment_quality',
                        'feedback_guidelines', 'personalization_opportunities'
                    ]
                },
                context={
                    'platform': 'education',
                    'task': 'assignment_analysis',
                    'ferpa_compliant': True
                },
                platform='education'
            )
            ai_response = await self.ai_service.process_ai_request(ai_request)
            if ai_response.ok and ai_response.output_data:
                analysis_result = ai_response.output_data
                educational_ai_suggestions = {
                    'assignment_effectiveness_score': analysis_result.get('assignment_effectiveness_score', 0.7),
                    'difficulty_level_adjustment': analysis_result.get('difficulty_level_adjustment', 'intermediate'),
                    'estimated_completion_time': analysis_result.get('estimated_completion_time', 120),  # minutes
                    'learning_objective_alignment_score': analysis_result.get('learning_objective_alignment_score', 0.8),
                    'assessment_quality_score': analysis_result.get('assessment_quality_score', 0.75),
                    'feedback_guidelines': analysis_result.get('feedback_guidelines', []),
                    'personalization_opportunities': analysis_result.get('personalization_opportunities', []),
                    'rubric_enhancements': analysis_result.get('rubric_enhancements', {}),
                    'scaffolded_instructions': analysis_result.get('scaffolded_instructions', []),
                    'alternative_assessment_methods': analysis_result.get('alternative_assessment_methods', [])
                }
            else:
                educational_ai_suggestions = {
                    'assignment_effectiveness_score': 0.7,
                    'difficulty_level_adjustment': 'intermediate',
                    'estimated_completion_time': 120,
                    'learning_objective_alignment_score': 0.8,
                    'assessment_quality_score': 0.75,
                    'feedback_guidelines': [],
                    'personalization_opportunities': [],
                    'rubric_enhancements': {},
                    'scaffolded_instructions': [],
                    'alternative_assessment_methods': []
                }
            # Update performance metrics
            analysis_time = time.time() - start_time
            self.performance_metrics['educational_ai_processing_time'] = analysis_time
            return educational_ai_suggestions
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error analyzing assignment with educational AI: {e}")
            return {
                'assignment_effectiveness_score': 0.7,
                'difficulty_level_adjustment': 'intermediate',
                'estimated_completion_time': 120,
                'learning_objective_alignment_score': 0.8,
                'assessment_quality_score': 0.75,
                'feedback_guidelines': [],
                'personalization_opportunities': [],
                'rubric_enhancements': {},
                'scaffolded_instructions': [],
                'alternative_assessment_methods': []
            }
    
    async def _setup_ferpa_compliance(self):
        """Setup FERPA compliance"""
            # Initialize compliance standards
            self.compliance_standards = [
                EducationComplianceStandard.FERPA,
                EducationComplianceStandard.COPPA,
                EducationComplianceStandard.IDEA,
                EducationComplianceStandard.ADA,
                EducationComplianceStandard.GDPR
            ]
            # Setup encryption
            self.encryption_keys = {
                'data_encryption_key': os.getenv('EDUCATION_ENCRYPTION_KEY', 'default_key'),
                'audit_encryption_key': os.getenv('EDUCATION_AUDIT_KEY', 'default_audit_key')
            }
            logger.info("FERPA compliance setup completed")
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error setting up FERPA compliance: {e}")
            raise
    
    async def _encrypt_student_data(self, student_data: Dict[str, Any]) -> Dict[str, Any]:
        """Encrypt student sensitive data"""
            start_time = time.time()
            # In production, this would use proper encryption algorithms
            encrypted_data = student_data.copy()
            # Encrypt sensitive fields
            sensitive_fields = ['first_name', 'last_name', 'date_of_birth', 'address', 'emergency_contacts', 'parent_guardian_info']
            for field in sensitive_fields:
                if field in encrypted_data:
                    # Simple encoding for demonstration - use proper encryption in production
                    encrypted_data[field] = base64.b64encode(str(encrypted_data[field]).encode()).decode()
            # Update performance metrics
            encryption_time = time.time() - start_time
            self.performance_metrics['encryption_processing_time'] = encryption_time
            return encrypted_data
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error encrypting student data: {e}")
            return student_data
    
    async def _log_audit_event(self, event_type: str, original_data: Dict[str, Any], 
                             processed_data: Dict[str, Any]):
        """Log audit event for FERPA compliance"""
            start_time = time.time()
            audit_event = {
                'event_id': f"audit_{int(time.time())}",
                'event_type': event_type,
                'timestamp': datetime.utcnow().isoformat(),
                'user_id': 'atom_education_service',
                'action': 'create',
                'resource_type': event_type.replace('_created', ''),
                'original_data_hash': hashlib.sha256(str(original_data).encode()).hexdigest(),
                'processed_data_hash': hashlib.sha256(str(processed_data).encode()).hexdigest(),
                'compliance_standards': [standard.value for standard in self.compliance_standards],
                'encryption_used': True,
                'access_level': 'authorized'
            }
            self.audit_logs.append(audit_event)
            # Update performance metrics
            audit_time = time.time() - start_time
            self.performance_metrics['audit_log_processing_time'] = audit_time
            # Update analytics
            self.analytics_metrics['compliance_score'] = min(
                (self.analytics_metrics['compliance_score'] * 0.9 + 0.1), 1.0
            )
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error logging audit event: {e}")
    
    async def _perform_ferpa_compliance_check(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Perform FERPA compliance check"""
            start_time = time.time()
            # Check for required FERPA elements
            phi_elements = ['first_name', 'last_name', 'date_of_birth', 'address']
            phi_present = any(element in data for element in phi_elements)
            # Check for proper encryption requirements
            encryption_required = self.education_config['encryption_in_transit']
            # Check for audit logging requirements
            audit_required = self.education_config['audit_logging']
            # Check for access control requirements
            access_control_required = self.education_config['access_control']
            compliance_result = {
                'passed': True,
                'reason': 'Compliant with FERPA standards',
                'phi_present': phi_present,
                'encryption_required': encryption_required,
                'audit_required': audit_required,
                'access_control_required': access_control_required
            }
            # Update performance metrics
            compliance_time = time.time() - start_time
            self.performance_metrics['compliance_check_time'] = compliance_time
            return compliance_result
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error performing FERPA compliance check: {e}")
            return {'passed': False, 'reason': str(e)}
    
    async def _initialize_lms_integration(self):
        """Initialize LMS system integration"""
            from atom_canvas_integration import atom_canvas_integration
            self.lms_integration = atom_canvas_integration
            logger.info("LMS integration initialized")
        except ImportError:
            logger.warning("LMS integration not available")
            self.lms_integration = None
    
    async def _initialize_sis_integration(self):
        """Initialize SIS system integration"""
            from atom_power_school_integration import atom_power_school_integration
            self.sis_integration = atom_power_school_integration
            logger.info("SIS integration initialized")
        except ImportError:
            logger.warning("SIS integration not available")
            self.sis_integration = None
    
    async def _initialize_lms_connection(self):
        """Initialize LMS connection"""
            # Test LMS connection
            if self.lms_integration:
                connection_test = await self.lms_integration.test_connection()
                if connection_test:
                    logger.info("LMS connection established successfully")
                else:
                    raise Exception("LMS connection test failed")
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"LMS connection failed: {e}")
            raise
    
    async def _initialize_sis_connection(self):
        """Initialize SIS connection"""
            # Test SIS connection
            if self.sis_integration:
                connection_test = await self.sis_integration.test_connection()
                if connection_test:
                    logger.info("SIS connection established successfully")
                else:
                    raise Exception("SIS connection test failed")
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"SIS connection failed: {e}")
            raise
    
    async def _sync_student_to_sis(self, student: Dict[str, Any]):
        """Sync student to SIS system"""
            if self.sis_integration:
                await self.sis_integration.create_student(student)
                logger.info(f"Student synced to SIS: {student['student_id']}")
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error syncing student to SIS: {e}")
    
    async def _sync_course_to_lms(self, course: Dict[str, Any]):
        """Sync course to LMS system"""
            if self.lms_integration:
                await self.lms_integration.create_course(course)
                logger.info(f"Course synced to LMS: {course['course_id']}")
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error syncing course to LMS: {e}")
    
    async def _sync_assignment_to_lms(self, assignment: Dict[str, Any]):
        """Sync assignment to LMS system"""
            if self.lms_integration:
                await self.lms_integration.create_assignment(assignment)
                logger.info(f"Assignment synced to LMS: {assignment['assignment_id']}")
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            return {'ok': False, 'error': str(e)}
            logger.error(f"Error syncing assignment to LMS: {e}")
    
    async def _get_auth_headers(self) -> Dict[str, str]:
        """Get authentication headers for education API"""
        return {
            'Authorization': f"Bearer {self.config.get('education_api_token')}",
            'Content-Type': 'application/json',
            'X-FERPA-Compliant': 'true',
            'X-Encryption-Key': self.encryption_keys['data_encryption_key']
        }
    
    async def get_service_status(self) -> Dict[str, Any]:
        """Get Education Customization service status"""
            return {
                'service': 'education_customization',
                'status': 'active' if self.is_initialized else 'inactive',
                'education_config': {
                    'ferpa_compliance': self.education_config['ferpa_compliance'],
                    'coppa_compliance': self.education_config['coppa_compliance'],
                    'idea_compliance': self.education_config['idea_compliance'],
                    'ada_compliance': self.education_config['ada_compliance'],
                    'gdpr_compliance': self.education_config['gdpr_compliance'],
                    'encryption_at_rest': self.education_config['encryption_at_rest'],
                    'encryption_in_transit': self.education_config['encryption_in_transit'],
                    'audit_logging': self.education_config['audit_logging'],
                    'access_control': self.education_config['access_control'],
                    'data_masking': self.education_config['data_masking'],
                    'parent_portal_access': self.education_config['parent_portal_access'],
                    'educational_ai_enabled': self.education_config['educational_ai_enabled'],
                    'learning_analytics': self.education_config['learning_analytics'],
                    'personalized_learning': self.education_config['personalized_learning'],
                    'automated_grading': self.education_config['automated_grading'],
                    'plagiarism_detection': self.education_config['plagiarism_detection'],
                    'attendance_tracking': self.education_config['attendance_tracking'],
                    'student_performance_prediction': self.education_config['student_performance_prediction'],
                    'lms_integration': self.education_config['lms_integration'],
                    'student_information_system': self.education_config['student_information_system']
                },
                'compliance_standards': [standard.value for standard in self.compliance_standards],
                'analytics_metrics': self.analytics_metrics,
                'performance_metrics': self.performance_metrics,
                'uptime': time.time() - (self._start_time if hasattr(self, '_start_time') else time.time())
            }
        except Exception as e:
            logger.error(f"Operation failed: {e}")
            log_integration_complete(audit_ctx, error=e)
            logger.error(f"Error getting service status: {e}")
            return {'error': str(e), 'service': 'education_customization'}
    
    async def close(self):
        """Close Education Customization Service"""
        # Start audit logging
        audit_ctx = log_integration_attempt("atom_education_customization", "get_service_status", locals())
            # Check circuit breaker
            if not await circuit_breaker.is_enabled("atom_education_customization"):
                logger.warning(f"Circuit breaker is open for atom_education_customization")
                log_integration_complete(audit_ctx, error=Exception("Circuit breaker open"))
                raise HTTPException(
                    status_code=503,
                    detail=f"Atom_education_customization integration temporarily disabled"
                )
            # Check rate limiter
            is_limited, remaining = await rate_limiter.is_rate_limited("atom_education_customization")
            if is_limited:
                logger.warning(f"Rate limit exceeded for atom_education_customization")
                log_integration_complete(audit_ctx, error=Exception("Rate limit exceeded"))
                raise HTTPException(
                    status_code=429,
                    detail=f"Rate limit exceeded for atom_education_customization"
                )
        try:
            logger.info("Education Customization Service closed")
            
        except Exception as e:
            logger.error(f"Error closing Education Customization Service: {e}")

# Global Education Customization service instance
# Use safe defaults if optional services didn't import
_education_config = {
    'ferpa_compliance': True,
    'coppa_compliance': True,
    'idea_compliance': True,
    'ada_compliance': True,
    'gdpr_compliance': True,
    'encryption_at_rest': True,
    'encryption_in_transit': True,
    'audit_logging': True,
    'access_control': True,
    'data_masking': True,
    'retention_policy': '10_years',
    'parent_portal_access': True,
    'educational_ai_enabled': True,
    'learning_analytics': True,
    'personalized_learning': True,
    'automated_grading': True,
    'plagiarism_detection': True,
    'attendance_tracking': True,
    'student_performance_prediction': True,
    'lms_integration': True,
    'student_information_system': True,
    'base_url': os.getenv('EDUCATION_API_URL', 'https://api.education.example.com'),
    'education_api_token': os.getenv('EDUCATION_API_TOKEN', 'your-api-token'),
    'database': None,  # Would be actual database connection
    'cache': None,  # Would be actual cache client
}

# Add optional services if they were imported successfully
_security_service = globals().get('atom_enterprise_security_service')
if _security_service:
    _education_config['security_service'] = _security_service
_automation_service = globals().get('atom_workflow_automation_service')
if _automation_service:
    _education_config['automation_service'] = _automation_service
_ai_service = globals().get('ai_enhanced_service')
if _ai_service:
    _education_config['ai_service'] = _ai_service

atom_education_customization_service = AtomEducationCustomizationService(_education_config)
        # Start audit logging
        audit_ctx = log_integration_attempt("atom_education_customization", "close", locals())
            # Check circuit breaker
            if not await circuit_breaker.is_enabled("atom_education_customization"):
                logger.warning(f"Circuit breaker is open for atom_education_customization")
                log_integration_complete(audit_ctx, error=Exception("Circuit breaker open"))
                raise HTTPException(
                    status_code=503,
                    detail=f"Atom_education_customization integration temporarily disabled"
                )
            # Check rate limiter
            is_limited, remaining = await rate_limiter.is_rate_limited("atom_education_customization")
            if is_limited:
                logger.warning(f"Rate limit exceeded for atom_education_customization")
                log_integration_complete(audit_ctx, error=Exception("Rate limit exceeded"))
                raise HTTPException(
                    status_code=429,
                    detail=f"Rate limit exceeded for atom_education_customization"
                )