Smart Student Productivity & Task Management System

Abstract

In modern educational environments, students often struggle to manage multiple academic tasks, assignments, and deadlines effectively. The “ Smart Student Productivity & Task Management System (StudiIn) “ is designed to provide a centralized platform that helps students organize tasks, track progress, and improve productivity while enabling administrators to assign tasks and monitor student performance efficiently. The system is developed using the MERN Stack (MongoDB, Express.js, React.js, Node.js) and includes features such as task management, progress tracking, productivity analytics dashboards, and role-based access control. The platform also integrates an AI-powered chatbot assistant to guide users and enhance the overall user experience. In addition, the system provides a secure password recovery mechanism using OTP-based email verification implemented through the Resend API. By combining task management, analytics, and intelligent assistance, the proposed system improves productivity and provides an effective solution for academic task monitoring.

Introduction

The text explains that students struggle to manage multiple academic tasks using traditional methods, while institutions also face difficulty in tracking student progress without a centralized system. To solve this, a web-based Smart Student Productivity & Task Management System is proposed, offering features like task creation, progress tracking, analytics dashboards, chatbot assistance, and secure authentication.

The literature review highlights existing task management tools, productivity systems, learning platforms, data visualization techniques, AI chatbots, and security methods, but identifies a gap in fully integrated solutions tailored for academic use.

The proposed system uses a MERN stack architecture (React, Node.js, Express, MongoDB) to provide a complete platform where students can organize tasks and administrators can monitor performance. It includes modules for authentication, task management, progress tracking, analytics, and chatbot support.

Results show that the system functions effectively, allowing efficient task management, real-time progress updates, insightful analytics, helpful chatbot interaction, and secure password recovery. Overall, the system improves student productivity, organization, and academic performance while enabling better administrative monitoring.

Conclusion

This paper presents the Smart Student Productivity and Task Management System (StudiIn), a web-based platform designed to help students manage academic tasks efficiently and improve productivity. The proposed system integrates important functionalities such as task creation, task assignment, progress tracking, analytics dashboards, and chatbot assistance within a single platform. The system is developed using the MERN stack (MongoDB, Express.js, React.js, and Node.js), providing a scalable and interactive solution for managing academic activities. The platform allows students to organize personal tasks, update task progress, and monitor productivity using visual analytics dashboards. Administrators can assign tasks to students and monitor their completion status, which helps in tracking student performance effectively. The integration of an AI-powered chatbot further enhances user experience by guiding users in navigating the platform and understanding different system features. In addition, the system implements secure authentication and password recovery using OTP-based email verification through the Resend API, ensuring reliable and secure access for users. Experimental testing results demonstrate that the proposed system improves task organization and productivity monitoring. Future enhancements may include mobile application support, intelligent task recommendation systems, and real-time notifications to further improve the functionality of the platform.

References

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Copyright

Copyright © 2026 Thoti Giri, Talari Naveen, Talari Nithin, Tallapalli Yeswanth Kumar, Tamisetty Pallavi, Mr. George Sebatian. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.