Validation of an Instrument for Assessing Iranian Higher Education Readiness for Artificial Intelligence Adoption: Exploratory and Confirmatory Factor Analysis

Mansoureh  Yousefi Chenari; Ebrahim Salehi Omran; Reza  Maniee

doi:10.61838/tw269835

Authors

Mansoureh Yousefi Chenari PhD Student in Higher Education, Faculty of Humanities and Social Sciences, University of Mazandaran, Babolsar, Iran Author https://orcid.org/0000-0002-2950-6334
Ebrahim Salehi Omran Professor, Department of Educational Sciences, Faculty of Humanities and Social Sciences, University of Mazandaran, Babolsar, Iran https://orcid.org/0000-0001-5546-7060
Reza Maniee Assistant Professor, Department of Futures Studies and Theorizing in Higher Education, Institute for Research and Planning in Higher Education (IRPHE), Tehran, Iran Author https://orcid.org/0000-0002-5359-5007

DOI:

https://doi.org/10.61838/tw269835

Keywords:

Artificial Intelligence Readiness, Higher Education, Instrument Validation, Exploratory Factor Analysis, Confirmatory Factor Analysis, Iran

Abstract

Purpose: This study aimed to develop and validate a context-specific instrument for assessing the readiness of Iranian higher education institutions for the adoption and application of artificial intelligence.

Methods and Materials: A sequential exploratory mixed-methods design was employed. In the qualitative phase, 25 participants, including artificial intelligence specialists, university faculty members, and employers or managers, were selected purposively and interviewed using semi-structured interviews. Thematic analysis generated the conceptual domains and initial questionnaire items. Following expert review and pilot testing, the instrument was administered to 248 faculty members and academic or educational managers using purposive-snowball sampling. Construct validity was examined using polychoric correlations, minimum residual exploratory factor analysis with Promax rotation, parallel analysis, Velicer's MAP, VSS, exploratory graph analysis, hierarchical clustering, and confirmatory factor analysis using DWLS/WLSMV estimation. Reliability, composite reliability, average variance extracted, and HTMT were also evaluated.

Findings: Sampling adequacy was excellent (KMO = 0.94), and Bartlett's test was significant (χ² = 7178.013, df = 1711, p < 0.001). Although alternative one-, two-, five-, and ten-factor solutions were examined, the five-factor structure provided the most defensible balance of fit, interpretability, and psychometric stability. In the full-sample CFA, the five-factor model showed CFI = 0.979, TLI = 0.976, RMSEA = 0.037, robust CFI = 0.930, robust TLI = 0.921, robust RMSEA = 0.053, and SRMR = 0.049. All factor loadings were significant (p < 0.001). Composite reliability ranged from 0.735 to 0.825, Cronbach's alpha from 0.751 to 0.829, and all HTMT values were below 0.85. The five factors represented research and industry interaction, governance and ethics, technological infrastructure, organizational culture and maturity, and education, skills, and employability.

Conclusion: The developed instrument demonstrated satisfactory reliability, structural validity, and discriminant validity and provides a promising framework for assessing institutional AI readiness in Iranian higher education, although further validation in larger independent samples is warranted.

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