Technology Needs and Readiness as Predictors of Vocational Students' Satisfaction with AI-Powered Adaptive Learning Systems

Abstract

The current vocational education curriculum faces significant challenges due to its inability to keep pace with the rapid development of technology-based industries. Innovative solutions, such as AI recommendation systems, are needed to bridge the gap by presenting relevant, personalized, and cutting-edge materials. This study aims to explain the influence of technological needs and readiness on the benefits, feature mapping, and their impact on consumer satisfaction of vocational students in AI recommendation systems. A quantitative approach with a causal associative research type was employed in this study. The population in this study were vocational students spread across four vocational education institutions in Aceh, namely the Aceh Polytechnic, the West Aceh State Community Academy, the South Aceh Polytechnic, and the Lhokseumawe State Polytechnic. The minimum sample size for the main study was determined by the Inverse Square Root Method (ISRM) based on the outcomes of the pilot study, for which data were collected from 183 respondents. The data were analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The results explain that technological needs and readiness significantly influence the expected benefits and feature mapping, which ultimately increases vocational students' satisfaction with the AI recommendation system. Thus, it can be concluded that the success of an AI system depends on a deep understanding of user needs and expectations, not just on its technical sophistication.