Data-Driven Decision-Making for Employee Training and Development in Jordanian Public Institutions

Introduction: AI-driven training and HR analytics have revolutionized employee development by offering personalized learning experiences and optimizing skill enhancement. Public institutions are increasingly leveraging AI-based recommendations and adaptive learning algorithms to improve workforce training. However, the effectiveness and challenges of these approaches in real-world applications require further investigation.Methods: This study employed a descriptive and analytical research design, utilizing both quantitative and qualitative methods. Data was collected from 385 employees in Jordanian public institutions using structured surveys and sentiment analysis of employee feedback. Statistical techniques, including regression analysis, ANOVA, and correlation analysis, were applied to assess the impact of HR data analytics, AI-based recommendations, and training personalization on training effectiveness.Results: The findings indicate that HR data analytics, AI-based recommendations, and training personalization significantly improve training effectiveness. Skill development emerged as the strongest predictor of training success (β = 0.7282, p < 0.001). Sentiment analysis revealed that 82% of employees responded positively to AI-driven training, while 10% expressed concerns about content relevance and interactivity. ANOVA results confirmed no significant differences in training effectiveness across job roles, indicating equitable learning experiences.Conclusion: AI-powered training is widely accepted but requires further refinement to address personalization challenges and employee engagement concerns. Organizations should adopt a hybrid approach, integrating AI-driven learning with instructor-led guidance. Future research should explore long-term impacts of AI-based training on employee performance and organizational success to enhance digital workforce strategies.