COVID-19 is considered a significant global health concern due to its high human-to-human transmission, leading to an increase in the number of infections and fatalities. Hence, the prevention of COVID-19 transmission and the reduction of mortality rates are contingent on the early detection of the disease. This study proposes an intelligent educational system which based on artificial intelligence techniques to assist doctors in medical educational institutions in the rapid detection of suspected instances of COVID-19 for preventing the virus from spreading. The system integrates three main components for quick detection and reducing infection spread. The first part involves diagnosis based on radiological images (chest X-Ray), the second part relies on medical analysis laboratory data, and the third part considers diagnosing based on crucial symptoms indicating the potential infection with coronavirus disease. The results from these three parts are combined to obtain the final diagnosis decision using a convolutional neural network (CNN) approach for chest X-ray image identification, the study demonstrates that the proposed method aligns closely with expert diagnoses, indicating high accuracy with 0.97%, and speed in identifying probable COVID-19 cases.