Author
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, making certain that users are not only aware of potential opportunities but also provided with guidance on how to pursue them effectively. The platform's evolution and adaptability to changing user needs and job market dynamics that is possible to integrating NLP for real-time interaction with 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 acqure 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 the methodologies with relevant technologies including angular and Node.js for front-end, Django and Python for back-end and PostgreSQL for data manipulations. 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 objective of the platform is to assist students and professionals in making informed job decisions and encourages the use of smart, data-driven tools in vocational development programs and educational institutions.
Suggested Citation
Natasha Shakeel,Saima Munawar,Nasir Naveed, 2026.
"Next Generation Career Counseling Platform Powered by Artificial Intelligence,"
International Journal of Innovations in Science & Technology, 50sea, vol. 8(2), pages 597-613, April.
Handle:
RePEc:abq:ijist1:v:8:y:2026:i:2:p:597-613
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