EXAMINING THE ACCEPTANCE OF ARTIFICIAL INTELLIGENCE TOOLS AND TECHNOLOGIES AMONG ENGINEERS AND TECHNICIANS IN TECHNICAL INSTITUTIONS: AN EMPIRICAL STUDY

Abstract

Higher education institutions (HEIs) now have more opportunities to improve technological, academic, and administrative performance due to the rapid development of Artificial Intelligence (AI). Unlike conventional technologies, AI systems have human-like capabilities that enable interaction, communication, learning, and decision support, making users’ emotional responses an important factor in technology acceptance. There is limited empirical evidence on the variables influencing engineers' and technicians' adoption of AI at technical educational institutions, despite the growing integration of AI technology across professional and educational contexts. Given AI's unique human-like capabilities, understanding technological and emotional determinants of its acceptance has become increasingly important. Here, we demonstrate how to empirically examine the factors influencing the acceptance of AI tools and technologies among engineers and technicians in Palestinian technical institutions. Using the Technology Adoption Model (TAM), the AI Device Use and Acceptance (AIDUA) framework, and the Technology–Organization–Environment (TOE) framework, this study aimed to investigate the adoption of AI tools and technologies. The research strategy used was a mixed-method approach. 101 engineers and technicians employed at Palestinian technical universities and colleges provided quantitative data, which Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze. Additionally, Max Weber Qualitative Data Analysis (MAXQDA) was used to assess qualitative responses to gain a deeper understanding of participants' opinions, experiences, and perceptions on the adoption of AI tools and technologies. The results showed that perceptions of the usefulness and ease of use of AI are significantly influenced by environmental and technological variables. The findings also showed that emotional reactions have significant effects on behavioral intention (BI) and are a key mediator through which perceived usefulness (PU) influences adoption of AI. The study concludes that users' emotional reactions to AI systems have a significant impact on acceptance of AI tools and technologies, which goes beyond conventional technological concerns. The findings add to the theoretical understanding of AI acceptance by merging TOE, TAM, and AIDUA into an integrated framework and give practical assistance for enhancing the adoption of AI applications inside technical educational institutions.