A Smart AI-powered study planning system built with the MERN Stack (MongoDB, Express, React, Node.js) that helps students create realistic and adaptive study schedules instead of rigid timetables.
Unlike traditional planners, this system analyzes study behavior, productivity, and completion rates to dynamically adjust study plans and reduce burnout.
Many students struggle with:
- Unrealistic study schedules
- Lack of consistency
- Overplanning or no planning
- Burnout during exam preparation
Traditional timetable apps create static plans that fail when students miss tasks.
AI Study Planner solves this by generating adaptive and personalized study schedules based on:
- Exam deadline
- Subjects and difficulty level
- Daily available study hours
- User productivity patterns
Instead of fixed timetables, the system dynamically adjusts the study plan based on user progress and behavior.
Creates a customized daily study schedule using exam date, subjects, skill level, and available hours.
Automatically adjusts the schedule when tasks are skipped or partially completed.
Tracks study patterns and suggests lighter schedules or recovery days when burnout risk increases.
Monitors focus sessions and suggests optimal study and break durations.
Generates a daily Focus Score (0–100) based on productivity, task completion, and study consistency.
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Email Verification on Signup
A verification email is automatically sent when a user creates an account. -
Account Activation Required
Users can log in only after verifying their email. -
Secure Password Encryption
User passwords are securely stored using hashing. -
OTP-based Password Reset
Users must verify an OTP sent to their email before changing their password. -
Protected Routes
Dashboard and study planner features are accessible only to authenticated users.
Architecture:
Hybrid AI System
- Rule-based scheduling engine
- LLM-powered suggestions
Shows the landing page introducing the AI Study Planner, key features, and call-to-action.
Secure authentication system for user account access and registration.
Allows existing users to securely log into the platform.
New users can create an account to start planning their study schedule.
This project is built through collaborative efforts.
Below are the team members who contributed to the development of the AI Study Planner + Productivity Tracker.
| Name | Role | GitHub Profile |
|---|---|---|
| Harsh Pandey | Frontend Development | GitHub |
| Ayansh Yadav | Backend Development | GitHub |
| Anmol Yadav | AI Logic & Scheduling Algorithm | GitHub |
| Abhay Singh | Analytics & Performance Tracking | GitHub |
| Anshuman Sharma | Testing & Documentation | GitHub |