A Systematic Evaluation of AI-Based Student Advisement and Course Projection Accuracy in Higher Education Institutions: An Analysis of Student Perceptions

Abstract

This study examines the perceptions of students in the Department of Computing and Information Sciences at the University of Technology and Applied Sciences (UTAS), Al Musanna, Sultanate of Oman, regarding the use of artificial intelligence (AI) in course advising. Data were collected from 125 respondents enrolled in B.Tech, Advanced Diploma, and Diploma programmes across three specialisations: Software Engineering, Cyber Networks, and Information Technology. A structured questionnaire using a five-point Likert scale was employed to assess five hypothesised constructs: Functional Reliability (L1), Predictive Recommendations (L2), Organisational Performance (L3), Student Readiness (L4), and Forecasting Capability (L5). The data were analysed using descriptive statistics, the Mann–Whitney U test, and Cronbach’s alpha to assess reliability. The composite mean scores ranged from 3.50 to 3.86, falling within the “Agree” category and indicating generally positive student perceptions across all five dimensions. Regarding group differences, specialisation exhibited greater statistical significance than gender or academic level. However, only the Organisational Performance construct demonstrated acceptable reliability (α = 0.779), while the remaining constructs exhibited questionable to poor reliability, suggesting that further refinement of the measurement instrument is necessary. Overall, this study contributes empirical evidence to the ongoing discussion on the integration of AI into higher education advising systems.