Modeling Student Acceptance of Augmented Reality in Science Education

This study investigates the factors impacting science and engineering students’ core technology acceptance model (TAM), attitude (ATT), and intention to use (ITU) on augmented reality (AR) educational tools in molecular biology by merging symmetric and asymmetric analyses via partial least squares structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). Rooted in the self-determination theory (SDT), flow theory, and TAM, data were gathered from 132 science and engineering students. The PLS-SEM findings confirmed six out of ten hypotheses, validating the significant role of autonomy (AUTO), perceived usefulness (PU), ATT, and ease of use, while showing non-significant effects for heart TAM constructs, such as PU → ITU. For an extended analysis of the linear approach, the fuzzy logic approach fsQCA was applied to derive causal configurations leading to high ATT and ITU. The fuzzy asymmetric analysis mapped several sufficient pathways with key combinations such as ATTPU and PUAUTO, validating high consistency and coverage. These results reveal the importance of investigating both aggregate effects and patterns to fully understand the acceptance of complex AR technology. Through the integration of symmetric and asymmetric analyses, this study provides refined insights into the perceptual and motivational factors leading to AR adoption in science education. These results contribute to both theoretical advancements and practical implications for the design of AR learning applications and tools that are immersive, engaging, and pedagogically effective.