Determinants of behavioral intentions and their impact on student performance in the use of AI technology in higher education in Indonesia: A SEM-PLS analysis based on TPB, UTAUT, and TAM frameworks

• 出版年: Social Sciences and Humanities Open
• 主要な著者: Muhammad Nurtanto, Septiari Nawanksari, Valiant Lukad Perdana Sutrisno, Husni Syahrudin, Nur Kholifah, Didik Rohmantoro, Iga Setia Utami, Farid Mutohhari, Mustofa Abi Hamid
• フォーマット: 論文
• 言語: 英語
• 出版事項: Elsevier 2025-01-01
• 主題: Artificial intelligence; TPB; UTAUT; TAM; Technology anxiety; Behavioral intention
• オンライン･アクセス: http://www.sciencedirect.com/science/article/pii/S2590291125003663

The rapid adoption of artificial intelligence (AI) technologies in higher education necessitates a comprehensive understanding of the key psychological and contextual factors influencing students' behavioral intentions and academic performance. This study aims to examine the determinants of students' intention to use AI and their impact on learning performance by integrating constructs from the Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), and Unified Theory of Acceptance and Use of Technology (UTAUT). A quantitative survey approach involved 2894 university students across diverse faculties in Yogyakarta, Indonesia, a prominent educational hub. Participants were selected using purposive sampling based on the criterion that they had utilized AI tools in their academic activities. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the measurement and structural models. The findings reveal that Attitude toward Behavior and Technology Anxiety significantly influence Student Performance through mediating variables such as Performance Expectancy, Facilitating Conditions, and Behavioral Intention to Use. Notably, perceived risk negatively affects behavioral intention and academic outcomes, suggesting high student awareness of data privacy, technological dependency, and accuracy uncertainty. The results emphasize the critical role of fostering positive attitudes and managing anxiety in enhancing AI-based learning performance. Furthermore, institutional support structures are pivotal in mediating the relationship between psychological factors and technology adoption. This study contributes to developing educational strategies by recommending targeted training programs, integrated technical support, and curriculum redesign to promote effective and responsible AI integration in higher education learning environments.