Artificial Intelligence Techniques for Attention-Based Sparse Graph Convolutional Neural Network-Based Forecast Model for Career Planning in Human Resource Management: Trends and Challenges

Abstract

The rapid evolution of artificial intelligence has significantly transformed human resource management by enabling data-driven decision-making and predictive analytics. Among various AI techniques, attention-based sparse graph convolutional neural networks have emerged as a promising approach for modeling complex relationships in structured HR data. This study presents a comprehensive review of artificial intelligence techniques applied to attention-based sparse graph convolutional neural network-based forecast models for career planning. The paper explores how graph-based representations capture employee relationships, skill dependencies, and organizational hierarchies while leveraging attention mechanisms to enhance interpretability and predictive accuracy. Sparse modeling further improves computational efficiency and scalability, making it suitable for large HR datasets. The review critically examines existing methodologies, highlighting their strengths in career trajectory prediction, talent management, and workforce optimization. Additionally, it identifies key trends such as hybrid deep learning architectures, integration of explainable AI, and real-time analytics in HR systems. Despite these advancements, several challenges remain, including data privacy concerns, model bias, lack of standardized datasets, and interpretability limitations. This paper provides insights into future research directions by emphasizing the need for robust, transparent, and ethical AI-driven HR forecasting systems. The findings aim to support researchers and practitioners in developing intelligent career planning tools that align organizational goals with employee growth.