Technology Acceptance of Generative Artificial Intelligence and Its Impact on Research Competencies Among Engineering Students in Mexico

Generative artificial intelligence adoption in higher education offers new opportunities for research competency development in engineering programs, yet empirical evidence on student acceptance and its academic impact remains limited, particularly in Latin American contexts. This study examined determinants of undergraduate engineering students’ acceptance of generative artificial intelligence tools and their perceived impact on research competency development. A cross-sectional survey was administered to 50 undergraduate engineering students at a Mexican national technology institute during the January–June 2026 semester. An extended Technology Acceptance Model with seven constructs was operationalized through a 21-item instrument; measurement quality was assessed via composite reliability and average variance extracted. PLS-SEM analysis was conducted in SmartPLS 4 with 5000 bootstrap resamples. Three direct structural paths were confirmed (p < 0.05): trust in AI results and perceived ease of use predicted perceived usefulness; perceived usefulness predicted intention to use. Formally tested indirect effects revealed that trust in AI results exerts a significant indirect influence on intention to use, fully mediated by perceived usefulness (β = 0.257, p = 0.004), constituting the primary adoption mechanism identified in the model. R2 (PU) = 0.396 and R2 (IU) = 0.492 indicate moderate to high explanatory power for the core adoption constructs. Intention to use was not significantly associated with academic performance (p = 0.366), suggesting that adoption intention and course-grade outcomes are governed by distinct determinants in this context. Trust and perceived usefulness are the central drivers of generative artificial intelligence acceptance, with implications for technology integration in Latin American engineering programs. This study contributes an empirically examined extended Technology Acceptance Model with confirmed indirect effects for developing-country higher education.