Education is vital in any type of society for the conservation of lives of its associates and the preservation of the public formation. The rationale of this chapter is not only to reveal the role of technology in education but also to reveal the factors affecting the proper utilization and adoption of technology in education. Prior studies carried out by researchers confirm that technology utilization and adoption in education undeniably helps teachers and learners in the teaching and learning process. This chapter serves as a stepping stone to support teachers to do better in utilizing and adopting technology in education to a certain extent as an alternative of overlooking their thoughts, efforts and desires in blindly trying to vie with the swift change of technology in education in this epoch. Hence, this chapter discusses technology in education, the roles of technology in education, factors associated with technology utilization and adoption in education and the factors that limit the proper utilization and adoption of technology in education.

Corrosion in metallic materials is a critical challenge in maintenance and safety, and traditional visual inspection methods are often time-consuming, labor-intensive, and dependent on human expertise, highlighting the need for more efficient and reliable solutions. Deep learning, particularly convolutional neural networks (CNNs), provides a promising approach by enabling automated and accurate image-based classification. This study investigates binary image classification of corrosion using four pre-trained CNN architectures, namely ResNet50, MobileNetV2, NASNetMobile, and EfficientNetV2B0, and integrates explainable artificial intelligence (XAI) techniques to provide interpretability and insight into each model’s decision-making process. A curated dataset of 4012 images, divided between corroded and non-corroded surfaces, was pre-processed, and augmented images resulted in a total of 9636 images used to train and evaluate the models. Performance was assessed through accuracy, confusion matrices, computational timing, receiver operating characteristic curves, precision–recall curves, and Cohen’s Kappa. In this paper, Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations are incorporated as an XAI technique to provide interpretable insight into the model’s reasoning process, enabling clear identification of corrosion regions and offering justification for each prediction produced by the system. A key contribution of this work is the integration of Grad-CAM to enhance explainability. The results showed that EfficientNetV2B0 demonstrates stable training with minimal sign overfitting compared to other models. MobileNetV2 achieved the lowest time to train with the datasets given, and ResNet50 achieved the highest classification performance in terms of confusion matrix, with an accuracy of 96.58%. Through Grad-CAM reasoning, EfficientNetV2B0 shows a specific high activation towards corroded regions compared to the other three models that were evaluated.