Adoption of Generative Artificial Intelligence in the Professional Projection of Industrial Engineering Students: A TAM-Based Model

Abstract

The rapid expansion of generative artificial intelligence tools is transforming both educational and professional environments, particularly in fields linked to industrial digital transformation. In this context, understanding the factors that influence future engineers’ willingness to integrate these technologies into their professional practice represents a relevant challenge for engineering education. The aim of this study was to analyze the factors influencing the intention to use generative artificial intelligence in the future professional practice of Industrial Engineering students, based on a conceptual model derived from the Technology Acceptance Model (TAM) and incorporating trust in AI-based systems. A cross-sectional quantitative study was conducted with a sample of 172 Industrial Engineering students from a Chilean university. The instrument was validated through expert judgment and assessed for internal consistency. The results show that perceived usefulness significantly predicts future professional intention to use generative artificial intelligence, while behavioral intention explains the projected willingness to integrate these technologies into professional contexts. The findings suggest that the adoption of generative artificial intelligence begins to emerge during university training, representing a potential indicator of the development of digital competencies aligned with Industry 4.0 environments.