Presenting a Structural Model of the University Policy-Making System with an Artificial Intelligence Development Approach in the Universities of Golestan Province

Abstract

This study was conducted with the aim of presenting a structural model of the university policy-making system with an artificial intelligence development approach in the universities of Golestan province. The research method was applied in terms of purpose, descriptive-survey in terms of data collection, and cross-sectional in terms of time. The statistical population included 28,716students from Farhangian University, Islamic Azad University, and the University of Medical Sciences in Golestan province. The sample size was determined to be 379individuals based on Cochran’s formula with a 95%confidence level and a 5%error margin, selected through stratified random sampling proportional to the population size. The data collection instrument was a researcher-made questionnaire titled “University Policy-Making System with an Artificial Intelligence Development Approach,” comprising 76items across six dimensions: core phenomenon, causal conditions, contextual conditions, intervening conditions, strategies, and consequences, with each dimension having two key components. The items were scored based on a five-point Likert scale. Face and content validity were confirmed by 10experts and the calculation of the Content Validity Ratio (CVR) and Content Validity Index (CVI). Construct validity was also confirmed through confirmatory factor analysis considering factor loadings above 0.5. The reliability of the questionnaire was calculated using Cronbach’s αcoefficient as 0.952for the entire instrument, indicating excellent reliability. For data analysis, descriptive statistics (frequency, percentage, mean, standard deviation) and inferential statistics, including structural equation modeling (SEM), were utilized using SPSS 23 and SmartPLS 3 software. The findings revealed that the model had a desirable goodness of fit. The artificial intelligence-based policy-making system had a positive, strong, and significant effect on all dimensions. The strongest relationships were observed with “consequences” (path coefficient = 0.934) and “causal conditions” (path coefficient = 0.867), respectively. The results indicate that artificial intelligence has the greatest impact on achieving desired outcomes and shaping the root causes of issues. However, the relationship with the “strategies” dimension was weaker (path coefficient = 0.394), indicating an existing gap between intelligent analyses and the formulation of operational strategies. In conclusion, the presented model is a valid framework for the intelligentization of university policy-making. For its successful implementation, it is recommended to place the formulation of a strategic document, the establishment of data-driven decision support centers, process redesign, human resources empowerment, and the development of native applied research on the agenda.