AI-Driven Full-Stack Approach to Personalized Career Guidance

Abstract

Background and aims: In today’s competitive job market, individuals struggle to craft resumes that effectively highlight their achievements and industry-relevant experiences. Existing career guidance systems are often fragmented and lack personalized, dynamic feedback. This study proposes an AI powered web platform, the Digital Career Coach, to address these gaps. Methods: The system was developed using a multi-tier architecture. The frontend utilizes Next.js and Tailwind CSS, while the backend is powered by Next.js API routes, Prisma ORM, and a Neon PostgreSQL database. The core analytical engine integrates the Google Gemini API to process user inputs using natural language processing (NLP) to generate industry-specific resume enhancements and mock interview simulations. Results: The implementation successfully analyzed user inputs (skills, education, interests) and generated highly accurate career paths and upskilling opportunities. The system demonstrated a 95% accuracy rate for contextual relevance and processed AI responses in 2.5–3.2 seconds, proving suitable for real-time interaction. Conclusion: The Digital Career Coach successfully showcases the potential of combining full stack web technologies with artificial intelligence to empower users in the job-search process. It provides a practical blueprint for leveraging AI in career-guidance systems to meet evolving workforce demands.

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