Overcoming the Academic-Industrial Divide: A Multi-Agent AI Structure for Optimizing Student Career Roadmaps

Abstract

The gap between what universities teach and what industries need because of new technologies is clear when looking at undergraduate students. This paper introduces an AI-powered career development tool made for students and recent graduates. The system uses a multi-agent design based on Google\'s Gemini 2.5 Flash to identify differences between a student\'s current skills and the skills needed for jobs in areas like blockchain, AI, or data science. It includes tools for building resumes that work well with job application systems, practice interviews that follow FAANG company standards, and customized learning plans that take 8-12 weeks. This approach could help provide fair and equal career guidance to all college students.

Introduction

The paper presents an AI-powered career development platform designed to address the growing “readiness gap” faced by B.Tech students, whose academic skills quickly become outdated and often do not align with industry requirements. This mismatch leads to difficulties in skill development, resume optimization, and interview preparation, along with confusion caused by excessive online information and lack of guidance.

To solve these issues, the system uses a multi-agent AI architecture powered by Google Gemini 2.5 Flash. It provides personalized services such as skill gap analysis, 8–12 week learning roadmaps, resume building and optimization for ATS systems, job matching, and mock interviews including FAANG-level preparation. The platform also integrates real-time job market data and supports structured, data-driven career guidance.

The literature review highlights existing solutions in AI-based career counseling, NLP-driven resume analysis, job recommendation systems, and adaptive learning platforms. While these technologies show progress in accuracy, personalization, and engagement, they still suffer from limitations such as lack of explainability, cold-start problems, weak guidance structure, and insufficient real-world validation.

The proposed system is built using a three-layer architecture (React frontend, Node.js backend, and Firebase database) and relies on strong prompt engineering strategies to ensure consistent and role-specific AI behavior. A multi-agent system divides responsibilities among specialized agents like resume analysis, job matching, roadmap creation, and interview simulation. Additional components include web scraping for job data collection and performance optimizations across frontend, backend, and AI processing.

Overall, the platform aims to provide a unified, intelligent, and personalized career guidance ecosystem that improves employability by combining skill assessment, learning guidance, resume optimization, and interview preparation into a single AI-driven system.

Conclusion

This research introduced an AI-based career development platform aimed at connecting academic learning with what employers need. The system uses a multi-agent setup powered by advanced language models to bring together important parts of career planning, such as identifying skill gaps, creating personalized career paths, improving resumes, matching users with suitable jobs, and helping with interview skills. The results show that the platform can produce organized, relevant, and useful information, helping users make smart choices about their careers. Using real-time data and smart agents ensures that the advice is tailored to each user and matches current industry needs. Overall, the system shows how AI can change the way people develop their careers by making the process easier, faster, and based on real data. The multi-agent design allows the system to be flexible and expandable while still offering high-quality suggestions throughout the user\'s career journey. This study adds to the growing area of AI in education and career support by showing how combined systems can make difficult decisions simpler and better prepare users for today\'s competitive job market.

References

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Copyright

Copyright © 2026 Rajat Verma, Rajeev Patel, Kapil Gangwar, Rajat Kumar. 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.