Adapting teaching and learning with existing generative AI by higher education Students: Comparative study of Zayed University and King Abdulaziz University

• Published in: Computers and education. Artificial intelligence Vol. 8; p. 100421
• Main Authors: Tbaishat, Dina, Amoudi, Ghada, Elfadel, Maha
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2025; Elsevier
• Subjects: Behavioral intention; Comparative study; GenAI adoption; Higher education; Student satisfaction; Teaching and learning; Student satisfaction; Higher education; Behavioral intention; GenAI adoption; Comparative study; Teaching and learning
• DOI: 10.1016/j.caeai.2025.100421
• ISSN: 2666-920X

This study examines the role of higher education students’ perceptions in adapting Generative AI (GenAI) tools for teaching and learning, with a particular focus on the factors that influence student satisfaction and engagement. A comparative approach is adopted, exploring student experiences at Zayed University (ZU) in the UAE and King Abdulaziz University (KAU) in Saudi Arabia. The principal variables of interest, including Expected Benefits (EB), University Support (US), Ethical Awareness (EA), and Technology Self-Efficacy (TSE), are examined, with particular attention to their direct and mediated influences through Behavioral Intention (BI) on student satisfaction (SS). Data were collected through surveys and analyzed using SmartPLS-4. The findings reveal notable similarities and differences between the two universities. At both ZU and KAU, BI demonstrated the strongest direct influence on SS, confirming its central role. EB and TSE significantly impacted SS both directly and indirectly through BI in both contexts, although their effects were stronger at KAU. Conversely, US and EA showed no significant direct or mediated effects on SS at either institution. R2 values indicated substantial explanatory power of the model, and Q2 values confirmed strong predictive relevance. These results suggest that while the core drivers (BI, EB, TSE) are consistent across contexts, institutional and cultural factors shape their relative impact. The findings highlight the importance of integrating GenAI tools into teaching practices and emphasize the role of student motivation, confidence, and institutional support in fostering effective adoption and enhancing learning experiences. Institutions should prioritize enhancing students' perceived benefits and technological self-efficacy by providing practical training and demonstrating the value of GenAI tools. These factors significantly influence student satisfaction and engagement, particularly in culturally distinct settings.