Students’ Intention toward Artificial Intelligence in the Context of Digital Transformation

Nikola Milicevic, Branimir Kalas, Nenad Djokic (), Borka Malcic and Ines Djokic
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Nikola Milicevic: Department for Trade, Marketing and Logistics, Faculty of Economics in Subotica, University of Novi Sad, Segedinski put 9-11, 24000 Subotica, Serbia
Branimir Kalas: Department for Financial and Banking Management, Faculty of Economics in Subotica, University of Novi Sad, Segedinski put 9-11, 24000 Subotica, Serbia
Nenad Djokic: Department for Trade, Marketing and Logistics, Faculty of Economics in Subotica, University of Novi Sad, Segedinski put 9-11, 24000 Subotica, Serbia
Borka Malcic: Department of Pedagogy, Faculty of Philosophy, University of Novi Sad, Zorana Djindjica 2, 21102 Novi Sad, Serbia
Ines Djokic: Department for Trade, Marketing and Logistics, Faculty of Economics in Subotica, University of Novi Sad, Segedinski put 9-11, 24000 Subotica, Serbia

Sustainability, 2024, vol. 16, issue 9, 1-15

Abstract: The analysis of students’ attitudes and perceptions represents a basis for enhancing different types of activities, including teaching, learning, assessment, etc. Emphasis might be placed on the implementation of modern procedures and technologies, which play an important role in the process of digital transformation. Among them is artificial intelligence—a technology that has already been found to be applicable in various sectors. When it comes to education, several AI-based tools and platforms can be used by students and teachers. Besides offering customized learning experiences, AI may play a significant part in establishing the concept of sustainability, especially when concerning the achievement of sustainable development goal 4. This paper investigates students’ intention to use artificial intelligence in education, taking three predictors from the UTAUT model and AI awareness as the moderator. The analysis included students from the Autonomous Province of Vojvodina, Republic of Serbia. For the purpose of the research, the partial least squares structural equation modeling (PLS-SEM) method was applied. Hereby, two models (without and with a moderator) were tested to examine the main and moderating effects, respectively. Regarding the results, while interaction terms were non-significant, the impacts of performance expectancy, effort expectancy, and social influence on behavioral intention were significant and positive.

Keywords: behavioral intention; UTAUT; artificial intelligence; education; digital transformation; AI awareness (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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