Adopting artificial intelligence in higher education: insights from the UTAUT framework on student intentions

The integration of Artificial Intelligence (AI) into higher education is gaining momentum, offering innovative tools that improve student engagement, support individualized learning, and enhance academic performance. While global statistics indicate that approximately 86% of students used AI for academic purposes, its adoption among Malaysian undergraduates remains relatively limited. This is despite ongoing national efforts such as the Malaysia Digital Economy Blueprint (MyDIGITAL), which seeks to accelerate digital transformation across sectors including education. Understanding the drivers and barriers to AI adoption in higher education is crucial for developing effective strategies that can enhance teaching and learning experiences. This study investigates the factors influencing students’ intentions to adopt AI in university settings. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), the study focuses on four core constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions. This study will employ a quantitative, crosssectional survey to examine factors influencing undergraduate students’ intention to use artificial intelligence (AI) in Malaysian universities. Data will be collected from a convenience sample of undergraduate students through an online questionnaire adapted from the UTAUT framework. A minimum of 137 participants will be targeted to ensure sufficient statistical power. The data will be analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS, which is suitable for exploratory research involving complex models. This study will extend the UTAUT framework to AI adoption among Malaysian undergraduates and will generate practical insights to guide educator training, institutional support strategies, and digital policy development under initiatives like MYDIGITAL.