Abstract The rapid and accurate diagnosis of COVID-19 remains a major challenge in medical imaging and artificial intelligence. To address this issue, this study proposes an optimized deep residual learning framework that integrates Particle Swarm Optimization (PSO) with the ResNet architecture for precise classification of chest CT images into five distinct categories: COVID-19, Normal, Pneumonia, Probable COVID-19, and Possible Pneumonia. The proposed hybrid model achieved an overall accuracy of 99.02% for this 5-class task. The proposed hybrid model employs PSO to fine-tune ResNet’s hyperparameters, thereby enhancing its convergence stability and classification accuracy. Multiple ResNet variants are trained and ensembled to improve generalization and robustness against noisy or imbalanced data. Comprehensive preprocessing and data augmentation strategies are applied to improve model resilience and prevent overfitting. Experimental evaluations on a large-scale CT image dataset demonstrate that the PSO-optimized ResNet ensemble achieves superior performance compared to conventional deep learning and optimization-based approaches, with an overall accuracy of 99.02%, sensitivity of 98.57%, and specificity of 98.02%. The results highlight the potential of combining metaheuristic optimization with deep residual learning to deliver a reliable AI-assisted diagnostic framework for rapid and accurate COVID-19 detection in clinical practice. Graphical abstract

Abstract The use of ChatGPT is rapidly increasing in higher education due to its applicability in several disciplines. Although ChatGPT offers several benefits to students and faculty, it is still in an early phase that requires research to support its integration into higher education institutions. This study systematically reviews the trends, benefits, and challenges of ChatGPT in Arab higher education. This study reviewed 40 articles indexed in Web of Science and Scopus between 2023 and 2025. ChatGPT trends include studies conducted in several Arab countries, article citations, research methodologies, and data analysis techniques employed by the researchers. The review of the articles identified personalized learning, skill development, learning experiences, and writing and translation as the main benefits of ChatGPT in Arab higher education. Finally, the study identified pedagogical adaptation, ethical concerns, limited geographical studies, and limited use of theoretical frameworks as the challenges of ChatGPT in HEIs. The current study’s systematic literature review will provide guidelines for educators, faculty, and researchers to understand the application of ChatGPT in higher education and maximize its potential benefits.