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The early and highly accurate prediction of COVID-19 based on medical images can speed up the diagnostic process and thereby mitigate disease spread; therefore, developing AI-based models is an inevitable endeavor. The presented work, to our knowledge, is the first to expand the model space and identify a better performing model among 10,000 constructed deep transfer learning (DTL) models as follows. First, we downloaded and processed 4481 CT and X-ray images pertaining to COVID-19 and non-COVID-19 patients, obtained from the Kaggle repository. Second, we provide processed images as inputs to four pre-trained deep learning models (ConvNeXt, EfficientNetV2, DenseNet121, and ResNet34) on more than a million images from the ImageNet database, in which we froze the convolutional and pooling layers pertaining to the feature extraction part while unfreezing and training the densely connected classifier with the Adam optimizer. Third, we generate and take a majority vote of two, three, and four combinations from the four DTL models, resulting in DTL models. Then, we combine the 11 DTL models, followed by consecutively generating and taking the majority vote of DTL models. Finally, we select DTL models from Experimental results from the whole datasets using five-fold cross-validation demonstrate that the best generated DTL model, named HC, achieving the best AUC of 0.909 when applied to the CT dataset, while ConvNeXt yielded a higher marginal AUC of 0.933 compared to 0.93 for HX when considering the X-ray dataset. These promising results set the foundation for promoting the large generation of models (LGM) in AI.

This study analyzed the impact of AI teaching on teaching quality, and revealed the mediating effect of student motivation and teacher expertise in the relationship of AI teaching and teaching quality.Based on the AI-TPACK theory, this study explored the impact of AI teaching on teaching quality and its mediating mechanism using questionnaires and AMOS structural equation modeling. The results show that AI teaching mainly improves teaching quality indirectly through enhancing student motivation and improving teacher expertise, rather than directly. This finding differs from the traditional view that AI teaching directly enhances teaching quality and emphasizes the important roles of students and teachers in AI teaching environments. The study provides some insights for educational policy makers and school administrators, pointing out that when implementing AI teaching strategies, emphasis should be placed on stimulating student learning motivation and teacher expertise growth in order to promote the optimization of teaching quality.