A Systematic Review of Skill Monitoring and Career Roadmap Prediction Using Computational Intelligence

Abstract

The fast-paced evolution of job markets and the dynamic changes in the skill requirements by the employers have urged
a need for intelligent systems that can support skill monitoring, predicting career roadmap, and job recommenda-tion. To address
these challenges, diverse computational intelli-gence techniques (including machine learning, natural language processing, and
recommendation systems) have been proposed over the last decade. In this paper, a systematic review of the existing approaches
in the areas such as skill-assessment, matching of resume with the job description, career prediction, and personalized
recommendation system is being presented. The studies reviewed have been classified on the basis of data sources, skill
representation approaches, modeling techniques, and evaluation metrics. A comparative analysis highlights the strengths and
limitations of current methods. Finally, open research challenges and future research directions are identified to guide the
development of scalable, adaptive, and explainable career guidance systems.