Next Generation Career Counseling Platform Powered by Artificial Intelligence

Natasha Shakeel; Saima Munawar; Nasir Naveed

Authors

Natasha Shakeel Faculty of Computer Science and Information Technology, Virtual University of Pakistan, Lahore
Saima Munawar Faculty of Computer Science and Information Technology, Virtual University of Pakistan, Lahore
Nasir Naveed Faculty of Computer Science and Information Technology, Virtual University of Pakistan, Lahore

Keywords:

Artificial Intelligence, Career Counseling, Machine Learning, Natural Language Processing, Job Market Trends, Career Recommendation, Skills Gap Analysis

Abstract

Individuals face a growing challenge in identifying career paths that align with their skills, interests, and goals in a world where career landscapes constantly shift due to technological advancements and global trends. By using machine learning algorithms, this platform seeks to bridge that gap by analyzing user profiles and providing personalized career recommendations. It ensures that users are not only aware of potential opportunities but also receive guidance on how to pursue them effectively on how to pursue them effectively. The platform evolves and adapts to changing user needs and job market dynamics through the integration of NLP for real-time interaction through an interactive career chatbot and a feedback-based learning system. A holistic approach to career development is ensured with features like skill gap analysis, job market trend monitoring, and educational resource suggestions. The aim of developing an AI-based career counseling platform is to give users precise, personalized career suggestions and acquire the knowledge and skills necessary to succeed in a diverse job market. The paper explores how to design and implement an AI-based career counseling platform, including methodologies and relevant technologies such as Angular and Node.js for front-end, Django and Python for back-end, and PostgreSQL for data manipulation. The platform is highlighted for its use of machine learning and natural language processing to offer personalized career guidance, analysis of skill gaps, and real-time job market insights. The experimental results show that the proposed system achieves a recommendation accuracy of 87.6% along with a precision of 0.85, a recall of 0.83, and an F1-score of 0.84, demonstrating strong predictive performance. The chatbot component responds to user queries with an average latency of 1.8 seconds and successfully resolves 86% of the queries, ensuring quick and effective communication. The skill gap analysis part accurately identifies gaps with an 82% accuracy rate, and overall user satisfaction is high at 89%, demonstrating the platform’s effectiveness and ease of use. The objective of the platform is to assist students and professionals in making informed job decisions and encourage the use of smart, data-driven tools in vocational development programs and educational institutions.

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