AI Carrier Navigation

Abstract

Choosing the right career path has become more complex for students. This is due to a lack of personalized mentorship and the presence of fragmented preparation tools. This research presents CareerGPT, an integrated AI-driven system designed to provide complete career support. Unlike traditional single-model systems, CareerGPT uses a multi-model synthesis approach. It uses the strengths of five advanced AI models. These include GPT-4.1, Claude 4, and Gemini 2.5, to ensure high-quality and verified guidance. The platform offers a set of important features. These are a 24/7 AI career counselor, an ATS-based resume analyzer that provides a 10-point quality check, a mock interview simulator with STAR-method feedback, and a real-time job matching engine. By bridging the gap between academic learning and industry requirements, CareerGPT gives job seekers data-driven insights and personalized roadmaps. The findings suggest that this unified approach significantly reduces the time spent on career research. It also improves overall interview readiness for students and young professionals.

Introduction

The text presents CareerGPT, an AI-powered platform designed to improve career planning and job preparation for students and job seekers by addressing the limitations of traditional, non-personalized career counseling and unreliable online information.

CareerGPT integrates multiple large language models (GPT-4.1, Claude 4, Gemini 2.5) using a multi-model synthesis approach, where responses from different AI systems are combined to produce more accurate, balanced, and reliable career guidance. The platform is built as a Next.js single-page application with a Node.js backend and MongoDB Atlas for scalable data storage.

Key features include:

• An AI resume analyzer that evaluates resumes, detects missing keywords, and provides ATS scoring with improvement suggestions
• A mock interview system using the STAR method to assess and improve user responses
• A career guidance engine that generates roadmaps and job recommendations using AI synthesis

The system improves reliability through multi-model redundancy and fallback mechanisms, ensuring high availability and reducing errors or hallucinations.

Results show strong performance, with the resume analyzer significantly improving ATS scores and identifying missing keywords in most cases. The multi-model approach also produces more accurate and balanced career advice compared to single-model systems.

Conclusion

CareerGPT successfully addresses the fragmentation in career guidance by providing a unified, AI-powered platform. By integrating five different Large Language Models, the system offers high reliability and comprehensive insights. The combination of resume analysis, mock interviews, and real-time job matching creates a powerful ecosystem for job seekers. Future enhancements will include voice-based mock interviews and deeper integration with LinkedIn for automated profile updates.

References

[1] Vercel Inc. (2024). Next.js Documentation. [Online]. Available: nextjs.org [2] MongoDB Inc. (2024). MongoDB Atlas: The Cloud Database. [Online]. Available: mongodb.com [3] J. Doe and S. Smith, \"Role of Artificial Intelligence in Modern Career Counseling,\" International Journal of Advanced Research in Computer Science, vol. 12, no. 3, pp. 45–50, 2022. [4] OpenRouter. (2024). Unified Interface for LLMs. [Online]. Available: openrouter.ai [5] S. Miller, \"The STAR Method for Competitive Interviews,\" Journal of Employment and Training, vol. 8, pp. 112–115, 2021. [6] M. Shell. (2002) IEEEtran homepage on CTAN. [Online]. Available: ctan.org

Copyright

Copyright © 2026 Rishikesh Arote, Geeta Pachore, Pragati Dabhade, Prasanna Tarle, Sivaram Ponnusamy, Umesh Pawar. 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.