The Role of AI Adoption and Its Relationship with Academic Performance among Industrial Engineering Students

Abstract

The use of Artificial Intelligence (AI) in higher education is reshaping students’ learning behaviors and academic performance. This study investigates how AI adoption, self-efficacy in AI learning, and design thinking mindset influence academic performance among Industrial Engineering students, with self-competence as a mediating factor. Data were collected from 80 undergraduate students at Universitas Atma Jaya Yogyakarta (cohorts 2021–2024) through a validated questionnaire.

The respondents included 31.25% freshmen, 23.75% sophomores, 26.25% juniors, and 18.75% seniors. Design Thinking, introduced early through labs and projects, is a key mindset in the Industrial Engineering curriculum and is explicitly applied in the Capstone Project. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze the relationships between constructs.

The findings indicate that self-efficacy in AI learning has a significant positive effect on both self-competence and academic performance. Similarly, design thinking mindset positively influences academic performance. However, AI adoption did not show a significant direct effect on academic performance. The mediating role of self-competence was not supported, as it did not significantly influence academic performance nor mediate the relationship between self-efficacy or design thinking and academic performance. These results underscore the importance of psychological traits such as self-efficacy and mindset in shaping academic outcomes in AI-integrated learning environments. The study contributes to AI-in-education research and offers insights for curriculum, instructional design, and institutional strategies to support academic success through technology adoption.