Advanced Web-Based Admission Automation: Spot Round Merit Processing with Dynamic Database Creation, Concurrency Handling, and Chatbot Support

Abstract

Manual academic admission processes are often time-consuming, error-prone, and inefficient, particularly when handling large-scale merit-based evaluations. This paper pro- posesafull-stackautomatedadmissionmanagementsystem that streamlines application handling, merit list generation, and student interaction using modern web technologies. The backend is developed using Node.js and Express, while MongoDB stores structured applicant data dynamically. Uploaded PDF merit lists are parsed using Python scripts and inserted into database collections for filtering and retrieval. The system features a secure admin login with JWT authentication, OTP-based email verification for applicants, and dynamic application form links.It also includes a built-in chatbot powered by a natural language processing (NLP) engine for answering applicant queries in real time. Experimental evaluations demonstrate that the system can efficientlyprocessmeritlistdata,generatecategory-basedfiltered results, and provide seamless user interaction. This work offersa scalable, intelligent, and secure solution suitable for digitizing the admission process in academic institutions

Introduction

Academic admissions in competitive fields face challenges like handling large applicant data, merit ranking, and ensuring fair selection. Traditional manual or basic digital methods lead to inefficiencies, errors, and poor user experience. To address this, the research proposes a comprehensive web-based admission system built on the MERN stack with Python integration for automated document processing, merit list generation, secure OTP-based authentication, and an AI-powered chatbot for applicant support.

The system offers distinct roles for admins and applicants, enabling admins to manage seat allocation, upload and parse merit lists, generate secure application links, filter data, and export results. Applicants verify identity via OTP, submit applications online, and receive real-time assistance through a chatbot. MongoDB’s flexible database design supports dynamic storage of diverse merit data.

The implementation emphasizes automation, security (JWT tokens, OTP), and interactive support, significantly reducing administrative workload and errors while improving transparency and scalability. Performance tests showed efficient document processing, prompt OTP delivery, responsive chatbot interaction, and robust handling of edge cases, demonstrating practical usability and reliability.

Conclusion

This investigation has documented the architectural framework and practical implementation of a computerised admission management solution that remedies significant constraints inherent in conventional merit-driven admission procedures. The engineered system incorporates contemporary web frameworks with computational document analysis and encrypted user verification protocols to establish a dependable and extensible infrastructure for educational institutions. Constructed utilising the MongoDB, Express.js, React.js, and Node.js technological ecosystem, supplemented by Python-derived PDF extraction utilities, the framework automates the acquisition of structured information from merit catalogues, facilitates applicant authentication through one-time password verification methodologies, and enables configurable merit list formulation with multi-parameter filtering capabilities. Furthermore, the incorporation of an interactive dialogue mechanism enhances participant engagement by facilitating instantaneous query resolution. Quantitative assessment verified the system\'s capacity to process substantial data volumes with minimal processing delays and considerable operational reliability. The structural design accommodates modular deployment, rendering it adaptable to diverse institutional requirements. These findings substantiate the system\'s capacity to markedly improve the efficiency, transparency, and accessibility of digitalised admissions frameworks.

References

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Copyright

Copyright © 2025 Anushree K. Kolte, Poorva S. Marne, Gauri S. Bhanage, Dhanashree A. Thombare, Dr. Sharmila K. Wagh. 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.