Predictive career guidance and entrepreneurial development for university students using artificial intelligence and machine learning

In today’s rapidly evolving job market, university students face unprecedented challenges in navigating their career paths. Traditional career guidance approaches sometimes fail to provide students with the knowledge and skills necessary for successful transitions from academics to the profession because of the changing nature of industries and the growing complexity of job possibilities. The study aims to explore the integration of AI and ML in providing predictive career guidance and entrepreneurial development for university students. This study proposes a novel Wild Horse Optimized Resilient Extreme Gradient Boosting (WHO-RXGBoost) model to predict the personalized recommendations that guide students in their career choices and entrepreneurial endeavors. University records and questionnaires are used to collect demographic data about students as well as information about any prior employment or entrepreneurial experience. The data was pre-processed using data cleaning and normalization using a robust scaler for the obtained data. The PCA feature extraction method is utilized to extract the datasets. By using this methodology, students can efficiently travel a massive amount of employment information by creating an information recommendation system that is customized to satisfy their requirements. The results indicate the proposed method outperforms traditional algorithms in providing relevant and timely career insights with metrics, such as F1-score (90%), precision (93%), accuracy (95%), and specificity (91%). User satisfaction indicates that technology considerably increases students’ experiences in entrepreneurship and CP. This research contributes to enhancing career outcomes and encouraging an entrepreneurial spirit among university students by providing a practical and effective response to the job issues experienced by students.