تعیین ابعاد و مؤلفه‌های الگوی برنامه درسی کارآفرینی‌محور با رویکرد هوش مصنوعی در دانشگاه‌های استان مازندران

سیده راحیل  هاشمی; ندا اسماعیلی; صغری افکانه

Authors

Seyedeh Rahil Hashemi . PhD student , Department of Educational Sciences, Ro.C., Islamic Azad University, Roudehen, Iran Author https://orcid.org/0009-0007-5336-6099
Neda Esmaeli Department of Educational Sciences, QaS.C., Islamic Azad University, Qaemshahr, Iran Corresponding author https://orcid.org/0000-0003-1481-5780
Soghra Afkaneh Department of Educational Sciences, Ro.C., Islamic Azad University, Roudehen, Iran Author https://orcid.org/0000-0002-4948-3694

Keywords:

Curriculum, Entrepreneurship, Artificial Intelligence, Data-driven Theory, Higher Education, Mazandaran Province

Abstract

The present study aimed to determine the dimensions and components of an AI-oriented entrepreneurship curriculum model for universities in Mazandaran Province. This applied study employed an exploratory sequential mixed-methods design consisting of qualitative and quantitative phases. In the qualitative phase, Glaserian grounded theory was used, and 20 experts and faculty members from universities in Mazandaran Province were selected through purposive judgmental sampling. Data were collected through semi-structured interviews and analyzed using open, axial, and selective coding. This phase resulted in the identification of 72 indicators, 22 components, and seven main dimensions. In the quantitative phase, a researcher-developed 72-item questionnaire was administered to 334 faculty members selected through stratified random sampling from a population of 2,578 faculty members. Exploratory factor analysis was used to examine construct validity, and Cronbach’s alpha was calculated to assess internal consistency reliability. The Kaiser–Meyer–Olkin measure of sampling adequacy was 0.871, and Bartlett’s test of sphericity was statistically significant (p<0.001), confirming the suitability of the data for factor analysis. Exploratory factor analysis extracted seven factors with eigenvalues greater than one, which collectively explained 69.10% of the total variance. The extracted structure consisted of entrepreneurial and intelligent competencies, AI and data competencies, intelligent curriculum design and structure, active and applied learning, human and professional competencies, infrastructure, faculty, and organizational support ecosystem, and assessment and program outcomes. Factor loadings ranged from 0.63 to 0.88. Cronbach’s alpha was 0.949 for the total instrument and ranged from 0.850 to 0.961 across the seven dimensions. The findings confirmed that the proposed AI-oriented entrepreneurship curriculum model has a coherent multidimensional structure, satisfactory construct validity, and high internal consistency and can provide a scientific basis for redesigning entrepreneurship curricula in universities of Mazandaran Province.

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