Survey on AI-Based Personalized Career Guidance and Preparation Systems

This paper presents a comprehensive survey of existing AI-driven career guidance systems, analyzing their methodologies, strengths, and limitations. The contemporary job market, characterized by rapid digital transformation and the entry of Generation Z, presents significant challenges for career navigation. Traditional career guidance methods are often insufficient, lacking the personalization and scalability required to meet modern demands. In response, recent research has focused on developing Artificial Intelligence (AI) and machine learning systems to provide more effective career support. Studies have proposed various models that offer personalized career recommendations by analyzing a user's academic performance, skills, and personal interests. Some systems enhance this by integrating psychological frameworks, such as the Myers-Briggs Type Indicator (MBTI), to match personality types with suitable job roles. The underlying technology often involves machine learning algorithms like Naïve Bayes and ensemble methods such as Random Forest to predict career paths with high accuracy. Beyond simple recommendations, the literature also explores AI's role in fostering career mobility by creating "skill bridges" and guiding users through upskilling pathways. A significant area of development is the creation of AI-powered chatbots and simulation systems that leverage Large Language Models (LLMs) to provide realistic, interactive interview practice and personalized feedback. While these specialized tools show promise, a recurring limitation identified across the research is the lack of a single, integrated platform that combines career discovery, skill development, and interview preparation into a seamless, end-to-end experience. The paper identifies key research gaps and outlines directions for future implementation of an integrated career guidance platform.