Unraveling the Drivers of Artificial Intelligence (AI) Adoption in Higher Education

• Published in: 2023 International Conference on University Teaching and Learning, InCULT 2023
• Main Author: Rodzi Z.M.; Rahman A.A.; Razali I.N.; Nazri I.S.M.; Abd Gani A.F.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190675516&doi=10.1109%2fInCULT59088.2023.10482651&partnerID=40&md5=f156ee0524909e44766c2c6e65038cb4

In the unfolding era of digital transformation, the integration of Artificial Intelligence (AI) in educational domains poses both unprecedented opportunities and challenges. As educational institutions globally grapple with the nuances of embedding AI into pedagogical frameworks, a pressing issue emerges: What drives the adoption of AI in these settings? This study was conducted out of the exigency to understand these drivers, and navigating the intricacies of AI acceptance among higher education stakeholders. The core research question (RQ) guiding this endeavor is: 'What factors predominantly influence the decision to adopt AI tools and methodologies in higher education?' Building upon data collected from 100 students via convenience sampling, regression analysis was employed to probe deeper into the dynamics. Preliminary insights underscored Cost-Effectiveness, Accessibility, and Customization as pivotal drivers, while Learning Outcomes, Efficient Learning, Adaptability, and Career Readiness registered lesser influence. Notably, the model's R2 values, signifying approximately 56.3% (Cox & Snell R Square) and 94% (Nagelkerke R Square) of the variance in AI adoption, highlighted a moderate influence of the designated variables. The model's integrity was further bolstered by the Hosmer-Lemeshow test, yielding a p-value of. 999. These findings form the bedrock for an upcoming, more granular exploration, utilizing the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, to discern the intricate causal pathways in the narrative of AI's integration in higher education. © 2023 IEEE.