Optimizing Career Pathways: A Deep Learning-Based Life Skills and Recommendation Framework

The evolving job market demands a dynamic approach to career guidance, integrating life skills assessment with intelligent recommendation systems. Traditional career counseling methods often lack personalization and adaptability, leading to suboptimal career choices among undergraduate students. This paper explores the potential of deep learningbased frameworks for optimizing career pathways by analyzing students’ life skills, academic performance, and behavioral traits. A hybrid deep learning model incorporating Natural Language Processing (NLP) for textual assessments, Convolutional Neural Networks (CNNs) for image-based skill recognition, and Recurrent Neural Networks (RNNs) for temporal pattern analysis is proposed. The system processes diverse educational and psychometric data sources to generate accurate career recommendations. The study also examines the impact of Explainable AI (XAI) in making career suggestions interpretable and trustworthy. Experimental evaluations demonstrate improved accuracy in career recommendations compared to traditional machine learning models. The proposed framework aims to bridge the gap between academic education and workforce requirements, equipping students with personalized career pathways aligned with their strengths and industry demands.