Design and Implementation of an AI-Based Career Advisor Using Cognitive Profiling and Skills Analytics

Abstract

Selecting the right career path remains a significant challenge for individuals due to the complexity of aligning skills, interests, and evolving industry demands. Traditional career counseling methods often suffer from subjectivity, limited scalability, and outdated information. This paper presents an AIdriven career advisory system that combines cognitive profiling, deep learning, and real-time job market analytics to deliver personalized and adaptive career recommendations. At the core of the system is a Long Short-Term Memory (LSTM) model trained on a large corpus of structured job data, enabling accurate prediction of suitable career roles based on user inputs such as skills, experience, and education. A companion course recommender suggests targeted online learning pathways to bridge skill gaps. Furthermore, the system integrates live labor market data via external APIs to ensure context-aware, up-to-date recommendations. An intelligent chatbot, powered by hybrid rule-based and generative language models, guides users through the process and enhances engagement. Together, these components form a comprehensive, interpretable, and responsive solution for students, job seekers, and professionals navigating modern career landscapes.