Understanding Behavioral Intention of AI Adoption: A Conceptual Framework Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) Model

Abstract

The twenty-first century brought Artificial Intelligence (AI) as a revolutionary technology which now transforms multiple sectors including business operations, educational systems, financial services and medical care delivery. While there are many positive impacts for student learning through Artificial Intelligence (AI), student usage and adoption of AI systems at higher education institutions continues to be variable. Student use of AI can be limited as a result of student perceptions about the reliability of AI systems, their own privacy when using AI, ethical concerns associated with the design and operation of AI systems, and perceived technical difficulties with AI. In addition, the literature on factors affecting student adoption of technology-based tools (such as AI systems) has presented contradictory evidence about which specific variables drive adoption behavior among students. Moreover, the literature provides little context about the nature of higher education institutions (HEI) in Malaysia and the applicability of those studies to institutions in Malaysia. This paper addresses the lack of empirical and contextual information on student adoption of technology-based tools such as AI systems by proposing a theoretical framework based upon an adaptation of the Unified Theory of Acceptance and Use of Technology (UTAUT). The proposed research defines how four fundamental predictors performance expectancy, effort expectancy, social influence, and facilitating conditions collectively impact the undergraduate students’ intentions to use AI. Unlike prior studies focused upon empirical tests of theories related to technology acceptance, this conceptual paper theoretically integrates current literature into a single, coherent framework along with testable hypotheses. From both theoretical and practical perspectives, the expected contributions of this study include providing a common basis from which to resolve the contradictions found in literature concerning the drivers of student adoption of AI by reassessing the original UTAUT constructs. Additionally, the proposed framework should provide the structured knowledge base necessary for higher education institution administrators, educators, and policymakers to create targeted strategies for integrating AI systems across all levels of institutional operations. Ultimately, future empirical testing of this framework should provide Malaysian HEIs with actionable data that may facilitate the development of explicit academic integrity guidelines; refinement of assessment designs; and creation of targeted AI literacy programs.