Artificial intelligence (AI) technology has emerged as a revolutionary tool in enhancing students’ learning and knowledge acquisition. Despite these benefits, many students remain unaware of AI’s potential or are hesitant to use it due to factors such as lack of knowledge, limited access, and concerns about reliability and data privacy. Previous studies have explored AI adoption in educational settings using models like the Technology Acceptance Model (TAM). However, findings have been inconsistent, prompting further investigation using the Unified Theory of Acceptance and Use of Technology (UTAUT). This study aims to examine the factors influencing students’ use of AI and its impact on learning satisfaction through the UTAUT framework. The research employs a quantitative method with convenience sampling, involving 130 university students. Data analysis is conducted using Structural Equation Modeling (SEM) with the Partial Least Square (PLS) approach. Results indicate that performance expectancy, effort expectancy, and social influence significantly affect AI adoption among students, while facilitating conditions do not show a significant impact. The study also finds that AI usage significantly increases learning satisfaction. These findings highlight the importance of addressing perceptions of performance, effort, and social influence to promote AI adoption in education.

Link: Adoption and utilization of artificial intelligence to enhance student learning satisfaction