The aim of this study is to examine the adoption of Generative AI (GenAI) tools among faculty in higher education using a revised Unified Theory of Acceptance and Use of Technology (UTAUT) model. A quantitative survey was conducted with 244 faculty members from eight Saudi universities, and the data were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS. The results show that Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), and Behavioral Intention (BI) significantly influence the adoption of GenAI tools. Among these, PE and EE had the strongest positive impact on BI, stressing the importance of user-friendly design and faculty training, while SI had a weaker but still meaningful effect. Gender and academic position were also found to moderate adoption behaviors, indicating differences across faculty groups. This study extends the UTAUT model by introducing new moderating factors and provides empirical evidence from Saudi Arabia. It further recommends intuitive system design, technical support, training, and the development of an AI-friendly academic culture to promote effective integration of GenAI in higher education.

Asphalt roads play a vital role in land transportation systems, significantly contributing to the growth and development of economies and societies. However, over time, the quality of these roads deteriorates due to aging and the cumulative effects of wear and tear, leading to various pavement and road issues such as potholes, cracks, and damaged sidewalks. This paper aims to develop a deep learning model, specifically leveraging the YOLOv8 object detection framework, to detect and classify road infrastructure problems using images captured from unmanned aerial vehicles (UAVs). The model processes a series of road images from the Roboflow dataset and was trained on Google Colab, utilizing advanced machine learning techniques to analyze the images and accurately identify road damage. Subsequently, the model was evaluated using metrics such as accuracy, recall, precision, and F1-score. The results demonstrated that the model is both efficient and reliable. The model achieved high performance, with an F1-score of 94 %, precision of 93 %, and recall of 95 %, which indicates its effectiveness in identifying various road defects. By detecting and locating issues such as potholes, cracks, and sidewalk damage, this model offers a promising solution for maintaining road infrastructure, supporting smart transportation systems, enhancing road safety, and helping reduce hazards and accidents.