Perception of Faculty Towards AI-Assisted Course Advising: An Empirical Investigation at UTAS Al Musanna

Abstract

This study is looking into how faculty feel about using Artificial Intelligence (AI) for course advising, specifi-cally in the Department of Computing and Information Sciences at the University of Technology and Applied Sciences (UTAS) – Al Musanna, Sultanate of Oman. The data was gathered from 122 respondents, including Diploma, Advanced Diploma, and B.Tech students. These students came from three specializations: Infor-mation Technology, Cyber Network, and Software Engineering. To do this, a structured questionnaire with a five-point Likert scale was used, and it aimed at five assumed themes: Functional Accuracy (H1), Smart Rec-ommendations (H2), Administrative Efficiency (H3), Faculty Readiness (H4), and Forecasting Capability (H5). The analysis involved descriptive statistics, Kruskal-Wallis non-parametric tests, and Cronbach's Alpha for reliability checking. Overall, the results point to fairly positive faculty perceptions on all five constructs, and the composite means were within the “Agree” area, roughly M = 3.50–3.86. When it comes to differences, the statistical significance showed up mostly with specialization, rather than gender or academic level, so there’s that. Reliability came out acceptable only for Administrative Efficiency (α = 0.779), while the other constructs were questionable to poor, meaning there is still room to refine the instrument. In the end, the study adds empirical support to the ongoing conversation about integrating AI into higher education advising systems.