Artificial Intelligence for Employee Growth: A Unified Model of Factors Influencing AI-Supported Staff Development Through SEM Analysis

The integration of Artificial Intelligence (AI) into professional development offers transformative opportunities for enhancing employee learning, performance, and competence. However, the determinants driving AI-supported staff development remain underexplored. This study investigates the key factors influencing employees’ behavioral intention to use AI systems for professional growth within organizational contexts. Drawing on a unified conceptual model that integrates the Technology Acceptance Model (TAM) and Social Cognitive Theory (SCT), the research examines how individual, organizational, and contextual enablers facilitate AI adoption in learning and development programs. Data was collected from 312 employees across diverse organizations participating in structured professional development initiatives and analyzed using Structural Equation Modeling (SEM). The results reveal that digital selfefficacy, management support for AI-related training, motivation for self-development, and perceived relevance of AI content are the most significant predictors of employees’ intention to engage in AI-enabled professional learning. The study advances theory by extending traditional technology adoption frameworks with motivational and contextual dimensions, offering a practice-centered understanding of AI adoption in employee development. The findings also underscore the importance of designing relevance-driven, confidence-building, and leadership-supported AI learning strategies to achieve sustainable digital transformation in workforce development.