Investigating the Factors Affecting the Effectiveness of Virtual Education Using Artificial Intelligence in the Educational System of Islamic Azad Universities in West Azerbaijan Province

Mehdi  Tabarrok Soureh; Taghi  Mohammadi

Authors

Mehdi Tabarrok Soureh PhD Student, Department of Management, Ara.C., Islamic Azad University, Tabriz, Iran Author https://orcid.org/0009-0002-2139-5200
Taghi Mohammadi Department of Management, Khoy.C., Islamic Azad University, Khoy, Iran Author https://orcid.org/0000-0002-1228-4579

Abstract

This study aimed to identify, prioritize, and validate the factors affecting the effectiveness of virtual education using artificial intelligence in the educational system of Islamic Azad Universities in West Azerbaijan Province. This developmental–applied study employed a sequential exploratory mixed-methods design. In the qualitative phase, experts in virtual education, educational technology, artificial intelligence, and higher education management were selected through purposive and snowball sampling, with interviews continuing until theoretical saturation. Semi-structured interviews were analyzed through thematic analysis, resulting in seven dimensions and 26 components. The extracted indicators were subsequently evaluated through a two-round Delphi process. In the quantitative phase, data were collected from 140 faculty members selected from approximately 300 faculty members of Islamic Azad Universities in West Azerbaijan Province. Fuzzy Analytic Hierarchy Process was applied to prioritize the identified dimensions, and Partial Least Squares Structural Equation Modeling was used to validate the measurement model and examine the relationships between the first- and second-order constructs. Kendall’s coefficient of concordance was statistically significant in the first Delphi round (W = 0.147, χ² = 110.866, p < .001) and increased substantially in the second round (W = 0.715, χ² = 747.653, p < .001), indicating strong final expert consensus. Confirmatory factor analysis showed that all first-order indicator loadings were statistically significant, with t values ranging from 10.032 to 49.261 (p < .001). All seven second-order dimensions also significantly contributed to the higher-order construct, with standardized loadings ranging from 0.784 to 0.840 and t values from 22.619 to 38.739 (p < .001). Thus, all hypothesized measurement relationships were statistically supported. The findings support a multidimensional model of AI-based virtual education effectiveness comprising educational, technological, psychological, social, managerial, human, and organizational dimensions. The validated model indicates that effective implementation requires coordinated attention to institutional management, technological readiness, human capabilities, social interaction, psychological preparedness, educational quality, and organizational support.

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