Smart Career Assistance Portal Using AI and Real-Time Industry Insights
As industries evolve rapidly, students and early-career professionals face increasing challenges in aligning their academic profiles with current market demands. Traditional career guidance systems often lack personalization, adaptability, and real-time insights. This paper presents an AI-powered career guidance platform designed to bridge this gap by offering an integrated set of tools, including an ATS-optimized resume builder, an intelligent cover letter generator, mock interview preparation, and weekly-updated industry insights. Built using Next.js and Shadcn UI, the platform features a responsive and intuitive user interface. Upon email-based login via Clerk authentication, users complete a profile form that captures their industry, specialization, experience, skills, and professional bio. The system then generates a dynamic dashboard displaying indemand skills and salary trends using data fetched weekly through Inngest. Resume generation is fully customizable and enhanced with AI-generated content tailored to the user’s domain. The intelligent cover letter module analyses job descriptions to craft role-specific documents. A mock interview module presents domain-aligned quizzes, tracks user progress across sessions, and provides AI-driven improvement suggestions. Content generation is powered by the Gemini API, a transformer-based model developed by Google. At the same time, a custom matching algorithm scores how well a user’s skills align with various job roles. Preliminary evaluations indicate improved job readiness, user engagement, and satisfaction. By unifying real-time labour analytics with AI-enhanced preparation tools, this system supports informed career decision-making and offers a scalable, personalized solution for modern employability challenges.
Authors
- Sameeha Afrin A
Publication Details
- Journal
- International Journal for Research in Applied Science and Engineering Technology
- Published
- 2025-05-14
- DOI
- https://doi.org/10.22214/ijraset.2025.70766
- Primary Topic
- Digital Transformation in Industry
- Type
- article
- Field-Weighted Citation Impact
- 0.00