An AI-Powered Hybrid Framework for Career Readiness: Job Role Prediction, Skill Gap Analysis, And Personalized Learning Path Recommendation
Vihansa Thathsiluni Chandrakumara
Department of Information and Communication Technology, Faculty of Humanities and Social Sciences, University of Sri Jayewardenepura, Nugegoda, Sri Lanka.
Maheesha Dhashantha Silva
*
Department of Information and Communication Technology, Faculty of Humanities and Social Sciences, University of Sri Jayewardenepura, Nugegoda, Sri Lanka.
*Author to whom correspondence should be addressed.
Abstract
Rapid digital transformation and evolving skill requirements have intensified career uncertainty and skill mismatches among information technology job seekers. Existing career-guidance tools often provide fragmented and static support, limiting personalised and future-oriented career planning. This study presents an AI-powered career-readiness platform designed to support job seekers in the information technology domain through integrated job-role prediction, skill-gap analysis, future skill-demand forecasting, learning-path recommendation, and resume optimisation. The system was developed as a web-based platform using a hybrid machine-learning and natural language processing architecture. A hybrid classifier based on a support vector machine and random forest was trained using an IT-domain resume dataset containing 10,174 records and 38 predefined job roles. The platform also incorporated text processing, semantic-similarity analysis, skill forecasting, and large language model-based resume feedback to generate personalised career-guidance outputs. Five publicly available datasets supported resume classification, job-role mapping, skill extraction, course recommendation, and skill-demand forecasting. The classifier was evaluated using an 80:20 stratified train-test split and five-fold stratified cross-validation. It achieved 99.90% accuracy and a weighted F1-score of 99.91% on the test set. Cross-validation produced a mean validation F1-score of 99.85%, with low variation across folds. Independent validation using a limited sample achieved 85% top-1 and 100% top-5 job-role accuracy, supporting real-world generalisation. Through a unified interface, the platform provides predicted job roles, identified missing skills, future skill trends, relevant learning pathways, and resume-improvement suggestions. The findings indicate that an integrated AI-based framework can provide structured and personalised career-readiness support for IT job seekers, although broader validation using diverse real-world datasets remains necessary.
Keywords: Career readiness, job-role prediction, artificial intelligence, machine learning, natural language processing, skill-gap analysis, skill-demand forecasting, learning-path recommendation, resume optimisation, career guidance