The purpose of this study was to investigate the published literature on human resource management and school performance from January 2012 to December 2022. Numerous literature evaluations have been conducted on human resource management and organizational performance, but school or teacher performance has received less attention than organizational performance. The PICOC (population, intervention, comparison, outcome, and context) technique is integrated into each stage of the PSALSAR framework to assure the study’s objective and comparability. This in-depth research is conducted in three stages: identifying pertinent keywords, screening pertinent papers, and selecting pertinent publications for review utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Mata Analysis) technique. This made a final database with 44 publications that met the study’s requirements for inclusion. This study reveals that HRM practices and school performance are correlated. The results of the research identify the eight most essential HRM practices for improving school performance, which included planning, organizing, recruitment and selection, training and development, performance management, employee relations and involvement, reward and compensation, health, safety, and work-life balance. Leadership style, motivation, satisfaction, productivity and task performance, competency, culture and climate, empowerment, and commitment were among the performance-influencing elements.

This research explores the advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries, highlighting its pivotal role in mitigating the global incidence of occupational incidents and diseases, which result in approximately 2.3 million fatalities annually. Traditional OHS practices often fall short in completely preventing workplace incidents, primarily due to limitations in human-operated risk assessments and management. The integration of AI technologies has been instrumental in automating hazardous tasks, enhancing real-time monitoring, and improving decision-making through comprehensive data analysis. Specific AI applications discussed include drones and robots for risky operations, computer vision for environmental monitoring, and predictive analytics to pre-empt potential hazards. Additionally, AI-driven simulations are enhancing training protocols, significantly improving both the safety and efficiency of workers. Various studies supporting the effectiveness of these AI applications indicate marked improvements in risk management and incident prevention. By transitioning from reactive to proactive safety measures, the implementation of AI in OHS represents a transformative approach, aiming to substantially reduce the global burden of occupational injuries and fatalities in high-risk sectors.