Perceived AI-Related Support and Sustainable Administrative Performance in Universities: The Role of Expert Systems, Automated Machine Learning, and Ease of Use

This study examines administrative staff perceptions of selected AI-related dimensions and their association with sustainable administrative performance at the University of Hail. Specifically, it focuses on expert systems, automated machine learning, and ease of use as perceived dimensions of AI-related administrative support. Methodology: A quantitative cross-sectional survey design was employed. Data were collected using a structured Likert-scale questionnaire administered to a purposive sample of 230 administrative staff members. Descriptive statistics and regression analysis were used to assess the perceived level of AI-related support and its association with sustainable administrative performance. Results: The overall perceived level of AI-related support was moderate, indicating partial integration of AI-related practices in administrative work. Expert systems, automated machine learning, and ease of use each showed a positive and statistically significant association with sustainable administrative performance. Expert systems showed the strongest standardized association. Collectively, the three dimensions explained a substantial proportion of the variance in sustainable administrative performance. Limitations: The study is limited by its cross-sectional design, reliance on self-reported questionnaire data, and focus on a single university using purposive sampling, which restricts causal interpretation and generalizability. The findings also reflect perceptions rather than objectively verified implementation data.