Designing and Validating a Model of Artificial Intelligence-Based Education in the Iranian Higher Education System: A Mixed-Methods Approach

Document Type : Research Paper

Authors

1 Department of Educational Governance and Human Capital, NT.C., Islamic Azad University, Tehran, Iran.

2 1. Department of Educational Governance and Human Capital, NT.C., Islamic Azad University, Tehran, Iran.

Abstract

The present study aimed to design and validate a model of artificial intelligence-based education in the Iranian higher education system using a mixed-methods (qualitative-quantitative) approach. In the qualitative phase, employing a case study method, the perspectives of faculty members and experts familiar with artificial intelligence in education selected through purposive sampling (prominent group) were collected via in-depth semi-structured interviews until theoretical saturation was reached. The data were then coded and analyzed using thematic analysis and NVivo software (version 8.8). In the quantitative phase, a researcher-developed questionnaire was designed based on the qualitative findings. The statistical population comprised faculty members from 20 top public and private (Azad) universities in Iran during the 2025-2026 academic year, from which 28 individuals with experience in the field of artificial intelligence were selected for validation using the prominent group sampling method. The questionnaire's validity was confirmed through item analysis (correlation coefficient of 0.65), and its reliability was confirmed through Cronbach's alpha (0.91). For model validation, Content Validity Ratio (CVR) and Content Validity Index (CVI) were employed with the participation of 28 experts. The qualitative findings led to the identification of three main components: antecedents (including the knowledge, skills, and attitudinal characteristics of faculty members and students, as well as the university's infrastructural characteristics and capacity-building), processes (including pre-teaching, during-teaching, and post-teaching activities), and consequences (including academic and psychological outcomes). In the quantitative validation, based on the CVR (minimum acceptable 0.33) and CVI (minimum 0.70) values, invalid items were removed, and items with values between 0.70 and 0.79 were revised. The final model, which encompasses the dynamic interaction of these components, possesses sufficient theoretical and empirical validity and can serve as a comprehensive framework for policymaking, curriculum planning, and faculty development aimed at the responsible and effective integration of artificial intelligence in Iranian higher education.

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Journal of Curriculum Research
Volume 16, Issue 1 - Serial Number 31
September 2026
Pages 130-103

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History

  • Receive Date: 26 February 2026
  • Revise Date: 25 May 2026
  • Accept Date: 01 June 2026

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