This article seeks to evaluate the use of artificial intelligence (AI) in the academic and professional development of undergraduate students in engineering programs in the department Norte de Santander, Colombia. The research uses structural equations based on partial least squares (PLS-SEM) to analyze the relationships between various variables that influence the adoption and effectiveness of AI in the educational process.

The study focuses on identifying how the incorporation of AI tools affects skills such as problem solving, creativity, critical thinking, and the ability to learn autonomously. A theoretical model is proposed that includes latent variables such as technological acceptance, perception of usefulness, digital skills, interaction with AI, and academic results. These variables are measured through surveys applied to engineering students from various educational institutions in the region under a Likert scale.

The PLS-SEM method is selected due to its ability to analyze complex relationships in contexts where samples can be moderate, as well as for its exploratory and predictive approach. The analysis includes construct validation through indicators such as average variance extracted, composite reliability, and R², allowing for the assessment of the influence of AI on academic outcomes and perceived readiness for the labor market.

The project's findings will contribute to identifying the strengths and challenges associated with the integration of AI in higher engineering education, offering specific recommendations to optimize its use in the region. Furthermore, this innovative approach provides a rigorous methodological framework for studying the impact of emerging technologies on education in similar contexts, promoting strategies that boost the academic and professional development of future engineers.

Keywords: Learning, structural equations, student, engineering, artificial intelligence.