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(SYS.02) — CLIENT SYSTEM — SCHOOL MANAGEMENT & AI

LearnMCA

AI-enhanced full-stack school management system integrating enrollment, academics, LMS, payments, attendance, communication, and intelligent analytics.

CLIENT / ORG: Maranatha Christian Academy FoundationYEAR: 2024–2026ROLE: Lead Full-Stack Developer & System Architect
LearnMCA School Management System Interface

(01) — System Overview

PROJECT
CONTEXT.

A full-stack school management platform built for Maranatha Christian Academy Foundation. Centralizes enrollment, student and personnel records, academic management, teacher assignments, spreadsheet-style grading, payments, RFID attendance, school services, learning resources, communication, and AI-assisted analytics in one institutional system. Features an integrated LMS with interactive activities and quizzes, offline grade entry with sync resilience, and multi-channel notifications.

The Operational Bottleneck / Problem

School operations are often fragmented across enrollment forms, spreadsheets, paper attendance logs, payment records, messaging tools, and separate learning platforms. This creates duplicated data, inconsistent records, slow administrative work, and disconnects between classroom activities and official academic records.

Engineering Solution

Engineered a unified school-management architecture that connects administrative operations, classroom workflows, financial records, learning resources, attendance, communication, and AI-assisted decision support through one role-based platform.

(02) — Architecture & Pipeline

SYSTEM
ARCHITECTURE.

Decoupled React/TypeScript Single Page Application communicating via REST endpoints with a PHP LavaLust MVC backend on MySQL, structured across core operational domains (Enrollment, LMS, Grading, Payments, RFID Attendance, Services, Announcements) with an AI intelligence layer handling conversational assistance, forward-looking predictive analytics, and sentiment processing.

Data & Execution Pipeline

01. React 18 / TypeScript Frontend
→
02. REST API & Auth Layer
→
03. PHP LavaLust Backend (Domain Services)
→
04. MySQL Institutional Database
→
05. AI Layer (Chatbot, Predictive Analytics, Sentiment)

(03) — Technical Focus

ENGINEERING
HIGHLIGHTS.

HL.01

Unified School Operations

Designed a single institutional data model connecting enrollment, academics, personnel, grades, payments, attendance, LMS activity, and school services.

HL.02

LMS + Academic Grade Synchronization

Built learning workflows where teachers can publish modules, upload materials, create interactive quizzes and activities, then synchronize LMS-generated results into official academic grade records alongside manual grading.

HL.03

Offline-First Grade Entry

Implemented resilient grade-entry workflows that allow teachers to continue recording grades during connectivity interruptions and synchronize queued changes once a connection is restored.

HL.04

RFID Attendance + Notifications

Integrated RFID-based entry and exit attendance with administrative monitoring and automated parent notification workflows through email and SMS.

HL.05

AI-Assisted School Intelligence

Integrated chatbot assistance, predictive analytics, and semantic/sentiment analysis to help users interpret institutional information and surface useful planning insights.

(04) — Implemented Capabilities

CORE
FEATURES.

01.

Online & manual enrollment with approval workflows and transferee tracking

02.

Spreadsheet-style teacher grade entry for rapid class-level encoding

03.

LMS module management with teacher-uploaded digital learning materials

04.

Interactive quizzes and learning activities with automated result capture

05.

LMS-to-academic grade synchronization alongside manual grading workflows

06.

Offline-capable grade entry with automatic synchronization on reconnect

07.

Academic period, subject curriculum, section, and advisory class management

08.

Role-based portals for Admins, Teachers, Students, and Personnel

09.

Hardware RFID gate attendance with entry/exit session detection

10.

Automated multi-channel attendance notifications via Email and SMS

11.

Tuition ledgering, flexible installment plans, and financial reporting

12.

School services management including uniform orders and service requests

13.

School-wide and section-targeted announcement broadcast system

14.

Student concern & administrative ticketing with communication history

15.

AI-Powered Academic Chatbot for conversational institutional guidance

16.

Model-Driven Predictive Analytics for enrollment & revenue trend projections

17.

Semantic & Sentiment Analysis on student inquiries and feedback tickets

18.

Administrative summary and institutional compliance reporting

(05) — Technology Stack

STACK
ARCHITECTURE.

Frontend & UI

React 18TypeScriptViteTailwind CSSRadix UITanStack QueryZustandRecharts

Backend & Core

PHPLavaLust 4.2.5 MVCREST-style APIJWT & Session AuthCustom Security HelpersOffline Sync Engine

Database & Storage

MySQL (Relational Schema)Indexed QueriesNormalized Academic Models

Attendance & Hardware

Hardware RFID IntegrationEmail Notification APISMS Dispatch APISession Matching

AI & Intelligent Analytics

AI Chatbot IntegrationModel-Driven Predictive AnalyticsSemantic & Sentiment AnalysisModel Inference ServicesHugging Face

Infrastructure

Hostinger Shared HostingCloudflareGit / GitHub

(06) — Problem Solving

TECHNICAL
CHALLENGES.

CHALLENGE 01

LMS and Academic Grade Synchronization with Offline Resilience

Engineered a spreadsheet-style grade entry interface with offline resilience allowing teachers to continue recording marks during network drops and sync later, alongside bidirectional synchronization connecting interactive LMS quiz scores into formal quarterly grade reports.

CHALLENGE 02

Protecting Student Data in AI Workflows

Enforced strict payload sanitization before sending queries to external model inference services, ensuring student PII (names, IDs, addresses, payment details) is excluded before external model processing.

(07) — Reliability & Governance

SECURITY & TESTING.

Privacy & Security Model

Includes login rate limiting (5 attempts / 5-minute lockout), password hashing, JWT bearer protection on APIs, role-based backend guards, and zero student PII transmission to external model services.