StudAI - AI-Powered Study Scheduler

An intelligent study planning platform that creates personalized schedules based on user preferences, commitments, and learning patterns.

Updated 5/19/2025

Next.js

Tailwind CSS

Node.js

Express

MongoDB

Python

TensorFlow

Google Auth

PDF Processing

Project Gallery

Overview

StudAI is a full-stack application that leverages artificial intelligence to generate optimized study schedules for students. The platform processes user inputs about class schedules, work commitments, study preferences, and course priorities to create balanced weekly plans. The AI considers peak concentration times, recommended study durations, and activity distribution to maximize learning efficiency. Built with Next.js for the frontend, Node.js for the backend, and integrated with machine learning models for schedule optimization.

Key Features

🧠 AI-generated personalized study schedules
📅 Multi-step onboarding for schedule preferences
📝 PDF schedule upload and text extraction
⏰ Time blocking for classes, work, and personal activities
📊 Activity distribution analytics
🎯 Module priority ranking system
🔄 Dynamic schedule adjustments
🔐 Secure authentication (Email + Google)
📱 Responsive design for all devices
📈 Progress tracking and recommendations

Technical Architecture

frontend:

Next.js with App Router, Tailwind CSS, React Hook Forms

backend:

Node.js with Express API

database:

MongoDB for user data and schedules

ai:

processing:

Python with TensorFlow for schedule optimization

models:

Time allocation algorithm
Concentration pattern recognition
Activity balancing system

integrations:

0:

Google OAuth for authentication

1:

PDF text extraction service

2:

Calendar API integration

Technical Challenges

Developing accurate PDF parsing for varied schedule formats
Creating AI models that adapt to individual learning patterns
Balancing multiple constraints in schedule generation
Implementing intuitive multi-step forms
Designing effective visualizations for schedule analytics
Ensuring real-time updates to generated schedules
Handling edge cases in time allocation
Optimizing AI model performance for quick responses