CampusHire: An AI-Powered Training and Placement Management System with Explainable Skill Matching

Abstract

Campus training and placement (T&P) activities in many engineering institutions are still coordinated through spreadsheets, e-mail and notice boards, which makes eligibility checks, shortlisting and placement reporting slow and error-prone and gives students little guidance on which opportunities suit them. This paper presents CampusHire, a web-based T&P management system that brings students, recruiters, training and placement officers and administrators onto a single role-based platform and adds an explainable skill-matching engine. The engine normalizes the skills recorded in each student profile and job posting, filters postings on academic eligibility and application deadline, and ranks the remaining postings by a skill-coverage score: the percentage of a job’s required skills that the student already holds. Because the score is computed from the set of missing skills, the same computation produces a skill-gap report that tells each student what to learn for a target role. The system is implemented with HTML5, CSS3, JavaScript and Bootstrap on the client, PHP on the server and a relational MySQL schema, and protects data with bcrypt password hashing, prepared SQL statements, session hardening and role-based access control. A worked example run on the implemented matching functions shows that ineligible postings are excluded before ranking and that, unlike the Jaccard index, the coverage score is not lowered when a student lists additional unrelated skills. CampusHire offers institutions a transparent, low-cost foundation that can be extended with semantic skill matching and placement analytics. Index Terms—Campus recruitment, content-based recommendation, explainable matching, job recommendation, placement management system, role-based access control, skill-gap analysis, web application.