Predicting the mental health of elementary school principals using artificial intelligence

Background and Objective: The present study was conducted with the aim of Predicting the mental health of elementary school principals using artificial intelligence. Methodology: The present study design was descriptive and correlational and predictive. The statistical population of the study included all primary school principals who were working in 1403-04. Accordingly, 128 people were selected from the aforementioned population by stratified random sampling and the necessary tools were administered to them. The sampling method was as follows: from among the schools, the primary school was selected and from among the districts, District 2 was selected. Due to the incompleteness of the questionnaires related to 4 of the respondents, the final analysis was conducted on 124 of them. To collect the information required for the study, the Keyes Mental Health Questionnaire (2002) and the Faraji et al. Management Artificial Intelligence Questionnaire (1402) were used. The collected data were analyzed in the SPSS-26 software using the multiple regression method. Findings: The results showed that the significance level for the variables AI management, AI-based decision-making, AI infrastructure, AI skills, and AI orientation is less than 0.05, so with 95% confidence, the existence of these five independent variables is significant for predicting mental health (β ≠0). Conclusion: This study deals with predicting the mental health of managers who use AI and shows that with the correct and ethical use of AI, healthier and more productive work environments can be created in which employees feel more satisfied and relaxed.