<![CDATA[Artificial Intelligence (AI) has recently gained prominence in higher education, particularly in Science, Technology, Engineering, and Mathematics (STEM) disciplines, offering transformative potential for learning and innovation. However, students’ adoption of AI tools is influenced by multiple psychological and contextual factors. This study aims to examine the effects of performance expectancy, effort expectancy, and social influence on students’ behavioral intentions to integrate AI into STEM education. A quantitative research design was employed, involving 203 undergraduate students from the University of Jambi and Universitas PGRI Mahadewa Indonesia. Data were analyzed using Structural Equation Modeling (SEM) through SmartPLS 3.3 to identify direct and mediating relationships among variables. The findings revealed that performance expectancy significantly influenced students’ behavioral intentions, indicating that perceived usefulness of AI outweighs ease of use in determining adoption. Effort expectancy also had a substantial effect and mediated the relationship between performance expectancy and behavioral intentions, while social influence showed no significant impact. These results highlight that students’ engagement with AI in STEM learning is driven more by perceived academic and functional benefits than by peer or social reinforcement. The novelty of this study lies in its integration of the Unified Theory of Acceptance and Use of Technology (UTAUT) framework with the STEM education context in developing countries, providing new empirical insights into AI adoption behavior. The study recommends designing AI-supported learning environments that emphasize practical benefits, user-friendly interfaces, and pedagogical integration to enhance students’ learning outcomes and technological readiness.]]>
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