Internship Portals: A Systematic Review Of Current Platforms And Future Directions

Abstract

In order to improve the efficiency of student recruiter matching, this study presents the design and implementation of an intelligent web-based internship portal. Current platforms mainly rely on keyword-based filtering, which frequently yields irrelevant results and limits personalized recommendations. To address this issue, the proposed system introduces a similarity-based matching approach that evaluates multiple factors, including student skills, academic performance, and individual preferences. The system is developed using a three-layer architecture with a responsive front-end, a backend powered by Node.js and Django, and a structured database for effective data handling. A prototype was tested using a dataset of 30 internship postings and 100 student profiles. According to the findings, the suggested method reduces application processing time from 12 seconds to 5 seconds while increasing recommendation accuracy from 62% to 84%. To improve dependability and user experience, the system includes secure authentication, recruiter verification, and real-time application tracking in addition to intelligent matching. The results show that combining scalable architecture with data driven recommendation methods can greatly increase internship accessibility and selection effectiveness. This work offers a useful basis for creating sophisticated internship platforms with improved performance, transparency, and personalization.