""" 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" )