CareerBridge AI: An AI-Assisted Smart Placement Management and Career Guidance Platform

Abstract

Campus placement management involves maintaining student information, managing company recruitment drives, checking eligibility, communicating opportunities, and supporting students in their preparation. Traditional approaches that depend on spreadsheets, manual records, and separate communication channels can increase the effort required for candidate verification and placement tracking. CareerBridge AI – Placement App is a centralized web-based platform developed to combine placement management with AI-assisted career guidance. The system uses Python and Django for backend processing, PostgreSQL for structured data management, and HTML, CSS, JavaScript, and Bootstrap for the user interface. It supports student registration with email OTP verification, profile and resume management, placement drive administration, automated eligibility verification, skill matching, skill-gap analysis, course recommendations, dynamic technical assessments, and an AI Career Assistant. A locally hosted Qwen 2.5 (1.5B) model through Ollama provides AI-assisted recommendations, while a rule-based fallback supports selected recommendation functions when the local model is unavailable. Mentor dashboards provide graphical analytics for placement monitoring. System testing documented successful workflows for OTP verification, multi-constraint eligibility checking, recommendation caching, AI-assisted skill updates, and mentor dashboard polling. The implemented system demonstrates how AI and web technologies can be integrated into a practical campus placement workflow while retaining clear scope for future enhancement.

Introduction

CareerBridge AI is a centralized, web-based campus placement management system designed to simplify recruitment activities and support students in preparing for employment opportunities. It addresses challenges associated with manual placement records, eligibility verification, student skill assessment, and career guidance by integrating these functions into a single platform.

The system provides separate features for students and placement mentors. Students can register using email OTP verification, manage their academic and professional profiles, upload resumes, explore placement opportunities, identify skill gaps, receive course recommendations, and practise technical assessments. Mentors can create and manage company placement drives, define eligibility criteria, review eligible candidates, and monitor recruitment progress through graphical dashboards.

CareerBridge AI uses Python, Django, PostgreSQL, HTML, CSS, JavaScript, and Bootstrap for application development. It integrates a locally hosted Qwen 2.5 (1.5B) language model through Ollama to support AI-assisted career recommendations, skill-gap analysis, course suggestions, technical assessments, and interactive career guidance. Chart.js provides placement analytics, while Gmail SMTP supports email verification.

A key feature is the automated eligibility engine, which checks students’ department, CGPA, active backlogs, and graduation batch against company requirements. The skill-matching component compares student capabilities with job requirements and identifies areas for improvement. Additionally, a rule-based fallback allows selected recommendation functions to continue operating when the AI service is unavailable.

Conclusion

CareerBridge AI – Placement App provides a centralized platform for managing campus placement activities and supporting student career preparation. By combining student profile management, placement drive management, automated eligibility verification, skill matching, AI-assisted guidance, technical assessment, and mentor analytics, the system addresses several limitations of fragmented and manually maintained placement workflows. The implementation demonstrates the practical integration of Django and PostgreSQL with a locally hosted Qwen 2.5 language model through Ollama. The documented tests confirm important workflows including authentication, OTP verification, eligibility checking, AI recommendations, assessments, and dashboard operations. The system therefore presents a structured foundation for AI-assisted campus placement management while keeping the limitations and future development requirements explicit.

References

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Copyright

Copyright © 2026 N. Nazmunisha, S. Sanjana, S. Siva Susila. 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.