AI-Based Career Guidance System For State And Central Government Examinations Using Multi-Agent Architecture

Authors: K. Kaethikeya Reddy, K. Naveen Kumar, E. Jithendra Reddy

Abstract: The rapid expansion of government employment opportunities in India has created a significant challenge for graduates and aspirants who lack structured access to personalized career guidance. This paper presents an AI-Based Career Guidance System specifically designed to assist students in identifying and preparing for state and central government examinations. The proposed system leverages a multi-agent artificial intelligence architecture inspired by the NexusAI framework, incorporating specialized agents for eligibility analysis, examination recommendation, syllabus guidance, and preparation strategy planning. The system integrates eligibility-based filtering, rule-based reasoning, and machine learning classification to deliver personalized examination recommendations aligned with the user's educational qualifications and interests. A web-based interface developed using HTML, CSS, JavaScript, and a Python backend with MySQL database management ensures accessibility and scalability. Experimental evaluation demonstrates that the proposed multi-agent approach enhances recommendation accuracy, user engagement, and preparation efficiency compared to conventional manual or single-agent advisory systems. The system addresses critical gaps in awareness, eligibility matching, and structured preparation planning for government examination aspirants.