Generative AI in higher education: a deep dive into educators’ concerns using the DEMATEL technique

Abstract

Purpose This research aims to explore educators’ perspectives on the detrimental effects of generative artificial intelligence (GenAI) on university students, providing insights that may not be apparent from a student-centered perspective. Design/methodology/approach The survey was conducted in two phases. In the first phase, data were collected from 278 faculty members, and seven key concerns were identified from an initial set of 17 criteria. In the second phase, these seven concerns were analyzed using the DEMATEL technique to assess their significance and establish causal relationships among them, based on the opinions of 17 experts. Findings The DEMATEL analysis identified inaccurate and misleading information, elevated cheating and shortcut practices and neglect of traditional resources as the most influential causal factors driving the detrimental effects of generative AI in higher education. These factors were found to trigger downstream outcomes such as a decline in writing proficiency, reduced creativity and originality and ultimately loss of unique voice and hindered skill development. The results reveal a hierarchical causal structure in which overdependence on GenAI progressively undermines students’ authentic learning, creativity and cognitive engagement, consistent with the propositions of media dependency theory. Originality/value While prior research has applied media dependency theory and DEMATEL separately, to our knowledge, this study is the first to combine them to examine educators’ concerns about GenAI. This integration offers a comprehensive view of how AI dependency influences academic behavior and provides a replicable framework for future research on technology-driven changes in education.

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