An Edutech Admission Consultancy Platform with AI College Predictor Module

Abstract

The rapid advancement of internet technologies and intelligent web-based systems has significantly transformed the educational ecosystem, especially in the area of admission counseling and academic guidance. Students seeking higher education often face difficulties while selecting suitable colleges and courses due to scattered information, complex counseling procedures, inconsistent cutoff records, and lack of proper guidance systems. Traditional educational consultancy services mainly rely on manual operations, physical counseling sessions, and static information delivery methods, which become inefficient when handling large numbers of students during admission periods. Furthermore, most existing admission guidance systems do not provide intelligent prediction support for estimating admission probabilities based on historical data and candidate performance. This research presents the design, development, and implementation of a Web-Based Edutech Admission Consultancy Platform integrated with an AI-Based Linear Probability College Predictor Module. The proposed platform provides centralized educational guidance, structured college information management, inquiry handling systems, chatbot assistance, and intelligent admission prediction functionalities within a single responsive web application. The AI-Based College Predictor analyzes candidate rank, category, preferred branch, and historical cutoff trends to estimate admission probability and classify institutions into Safe, Moderate, and Dream categories. The proposed platform is developed using HTML5, CSS3, Bootstrap 5, JavaScript, PHP, and MySQL technologies. The system supports secure database management, responsive user interfaces, chatbot communication mechanisms, and scalable administrative operations. Experimental analysis demonstrates that the platform improves counseling efficiency, reduces manual effort, enhances accessibility, and simplifies institutional selection processes for students. The modular architecture also supports future scalability for cloud deployment, machine learning integration, mobile applications, and advanced predictive analytics systems.