Digital College Forms Management System: A Web-Based Platform for Automated Institutional Documentation
Dr Krishna Anneboina, Assistant Professor, Department of Computer Science and Engineering (Internet of Things), Guru Nanak Institutions Technical Campus
N Jaya Prakash Reddy, Department of Computer Science and Engineering (Internet of Things),
Guru Nanak Institutions Technical Campus, 22-6944,22wj1a6944@gniindia.org
Nenavath Vijay Devgan, Department of Computer Science and Engineering (Internet of Things),
Guru Nanak Institutions Technical Campus, 22-6947,22wj1a6947@gniindia.org
U Sandeep Kumar Reddy, Department of Computer Science and Engineering (Internet of Things),
Guru Nanak Institutions Technical Campus, 22-6960,22wj1a6960@gniindia.org
G Pavan Kumar, Department of Computer Science and Engineering (Internet of Things),
Guru Nanak Institutions Technical Campus, 23-6903,23wj5a6903@gniindia.org
Abstract - — Administrative workflows in educational institutions continue to rely heavily on paper-based procedures, causing significant delays, inconsistencies, and resource wastage. This paper introduces EduForms, an intelligent, web-based institutional forms management platform built on the MERN stack — MongoDB, Express.js, React.js, and Node.js. The platform eliminates manual intervention through a combination of automated multi-level approval chains, role-based access control across eight distinct user roles, and intelligent AI-assisted form discovery. A Two-Factor Authentication mechanism utilizing One-Time Passwords enhances login security. An embedded AI assistant called EduBot allows users to locate appropriate forms through natural language interaction, while a universal voice-based form-fill module enables field population through spoken input — supporting all form types and fields. A smart approval automation engine evaluates submissions against predefined rules and automatically approves routine requests, while a priority-based routing mechanism escalates urgent applications directly to higher authorities. A predictive analytics widget presents approval probability estimates derived from historical submission data. The system supports forty-nine form templates spanning eight role-specific portals and includes a contextual feedback mechanism and a comprehensive exam branch management portal. Experimental evaluation confirms measurable reductions in processing time, improved submission accuracy, and a substantially enhanced user experience compared to conventional manual systems.
Keywords: MERN Stack, Intelligent Form Management, Two-Factor Authentication, AI-Assisted Workflow, Voice Form Fill, Priority-Based Routing, Smart Approval Automation, Real-Time Tracking, Role-Based Access Control
