Factor structure of artificial intelligence use in university teaching pedagogy

Abstract

The use of artificial intelligence (AI) in higher education pedagogy has gained increasing relevance; however, there remains a shortage of psychometrically validated instruments capable of assessing, in a multidimensional and context-specific manner, faculty adoption of these technologies. The aim of this study was to identify the factorial structure and evaluate the psychometric properties of an instrument designed to measure AI use among university faculty. The instrument was developed based on the unified theory of acceptance and use of technology and was administered to a convenience sample of 330 university instructors. Exploratory factor analysis revealed a robust seven-factor structure explaining 66.90% of the total variance, allowing the refinement and optimization of the instrument from 50 to 30 items. The final scale demonstrated excellent overall internal consistency (Cronbach’s alpha = 0.913). It is concluded that the developed instrument is a valid and reliable tool for higher education institutions to assess and diagnose faculty levels of AI adoption, thereby facilitating the design of context-specific institutional policies and effective professional development programs in AI-enhanced pedagogy.