Student Well-Being in Higher Education: A Study Demands–Resources Theory Approach to Academic Stress, Workload, Institutional Support and Resources, and AI Integration
Article Number: e2025557 | Available Online: November 2025 | DOI: 10.22521/edupij.2025.19.557
Rawan Khaled Diabat , Mehmet Şükrü Bellibaş
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Abstract
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Background/purpose. Psychological well-being is a key factor in student success and a priority in global education reform, as reflected in Sustainable Development Goal 3, which promotes Good Health and Well-being. This study examines the factors influencing university students’ perceived well-being in the context of higher education in the United Arab Emirates, a context that has received less attention in higher education research despite significant government investments. Guided by the Study Demands–Resources (SDR) theory, the research examines how academic demands, institutional resources, and experiences with artificial intelligence (AI) relate to student well-being. Materials/methods. The research used a cross-sectional, quantitative survey, collecting data from 265 university students in the UAE. The study applied descriptive statistics, correlation, and regression analyses to examine the relationships between academic demands, institutional resources, and student well-being. Results. We found that academic workload acted as a significant demand on students, serving as the strongest predictor of stress and a significant negative predictor of well-being. Conversely, AI use emerged as a key resource, positively predicting student well-being. Other institutional resources, such as lecturer support and growth opportunities, were not significant predictors of well-being or stress. Demographic analysis showed that female students reported significantly higher wellbeing. |
Conclusion. Our research showed that higher education institutions should consider AI and workload as key resource and demand factors to affect student wellbeing. As AI emerges as a critical new resource, universities are advised to adopt responsive strategies that integrate AI tools while simultaneously addressing student mental health. This could reduce the perceived workload and stress level and support wellbeing.
Keywords: Student well-being, academic stress, institutional support, SDR theory, AI in education
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