EVALUATING DRIVERS OF AI WRITING TOOL ADOPTION AMONG UNDERGRADUATES AT UITM NEGERI SEMBILAN: AN EWM-DEMATEL APPROACH
DOI:
https://doi.org/10.35631/JISTM.1144049Keywords:
DEMATEL, EWM, AI in Higher Education, Technology Adoption, UndergraduateAbstract
Academic writing and completing assignments can be challenging for undergraduate students, particularly when they are required to use disciplinary vocabulary, organize their ideas coherently, and apply analytical reasoning beyond basic coursework. With the growing availability of artificial intelligence (AI) writing assistance tools, students can now obtain writing-related support whenever they need it. However, the factors that influence students’ use of these tools and the relationships among these factors are still not well understood. Therefore, this study examined the factors that drive undergraduates to use AI writing assistance tools. The study focused on students from the Faculty of Computer and Mathematical Sciences (FSKM) at Universiti Teknologi MARA (UiTM) Seremban Campus, Malaysia. An integrated Entropy Weight Method (EWM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach was used to examine the eight identified adoption drivers. EWM was used to determine the objective weights of the criteria based on the variation in students’ responses, while DEMATEL was used to examine the causal relationships among the drivers. Data was collected through a structured questionnaire from 27 undergraduate students who reported using AI writing tools on a daily basis and were therefore selected as the experts for this study. The results showed that Security and Privacy was the highest-ranked driver (Rank 1) and was classified as a Cause driver. This was followed by Learning Support and Motivation (Rank 2) and Cost and Value (Rank 3), which were also classified as Cause drivers. In contrast, Ease of Use (Rank 6) and Accessibility (Rank 8) were classified as Effect drivers. This suggests that students’ expectations regarding usability and accessibility may be influenced by more fundamental concerns related to trust, educational support, and perceived value. Overall, the findings indicate that institutions should pay attention not only to the usability of AI writing tools but also to the underlying factors that shape students’ willingness to use them. The findings may help e-learning designers and higher education administrators identify areas that require greater attention when developing policies and support for the responsible use of AI writing assistance tools.
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