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Artificial intelligence adoption test based on UTAUT2 and complex thinking: design with K coefficient and reliability analysis using structural equation modeling

Abstract:

Incorporating Artificial Intelligence (AI) as a technological tool to strengthen training processes can potentially increase pedagogical quality and learning experiences; however, analyzing the acceptance of this technology within the complexity of university environments is necessary. This research aims to analyze a test’s design, validation, and reliability to assess the factors that guide the acceptance of AI in higher education. The study’s questionnaire design incorporated the interplay between the unified theory of adoption and use of technology (UTAUT2) and the complex thinking paradigm (CT) in a model. It was validated by applying the K coefficient to select the experts and the Simplified Digital Delphi method. Subsequently, a reliability analysis used a structural equation model. The results indicate that the questionnaire reliably evaluated students’ acceptance of artificial intelligence applications within the complexity of higher education. It demonstrated that the strategy to analyze the validation design of the instrument’s reliability could be replicated to systematically produce new questionnaires that measure the acceptance of emerging technologies. The study concludes that the questionnaire and the methodology can serve as a reference to measure the acceptance of technologies in universities.