A Machine Learning Approach to Identify Training Needs and Skill Gaps in Human Resource Development

Abstract

In the rapidly evolving landscape of human resource development (HRD), identifying training needs and skill gaps is crucial for enhancing workforce performance and organizational effectiveness. This study employs a qualitative approach, utilizing a comprehensive literature review to explore the application of machine learning techniques in HRD. The research examines various machine learning algorithms and their efficacy in analyzing employee performance data, job descriptions, and industry trends to pinpoint specific training requirements and skill deficiencies. By synthesizing existing studies, the paper highlights the transformative potential of machine learning in streamlining the training needs assessment process, enabling organizations to make data-driven decisions. Furthermore, the findings reveal that integrating machine learning into HRD practices not only enhances the accuracy of identifying skill gaps but also fosters a proactive approach to workforce development. This study contributes to the existing body of knowledge by providing insights into how machine learning can be leveraged to optimize training programs and align them with organizational goals. The implications of this research extend to HR practitioners and organizational leaders seeking innovative solutions to enhance employee capabilities and drive sustainable growth. Ultimately, the study underscores the importance of embracing technology in HRD to stay competitive in an increasingly dynamic business environment.