###### Abstract  
The COVID-19 pandemic has made the world focus on providing effective and fair online learning systems. As a consequence, this paper proposed a new intelligent, fair assessment of online examinations for virtual and remotely controlled laboratory experiments running through Laboratory Learning Systems. The main idea is to provide students with an environment similar to being physically present in a Laboratory while conducting practical experiments and exams and detecting cheating with high accuracy at a minimal cost. Therefore, an intelligent assessment module is designed to detect cheating students by analyzing their mouse dynamics using Artificial Intelligence. The mouse interaction behavior method was chosen because it does not require any additional resources, such as a camera and eye tribe tracker, to detect cheating. Various AI algorithms, such as KNN, SVC, Random Forest, Logistic Regression, XGBoost, and LightGBM have been used to classify student mouse behavior to detect cheating, and many metrics are used to evaluate their performance. Moreover, experiments have been conducted on students answering online laboratory experiments while cheating and when answering the exams honestly. Experimental results indicate that the LightGBM AI algorithm achieves the best cheat detection results up to an accuracy of 90%, precision of 88%, and degree of separation of 95%.  
online laboratory; LLS; cheating detection; mouse dynamics; AI algorithms +
Footnote †: journal: Medical  
fair system, an intelligent cheating detection technique is needed, which is presented in detail in this paper.  
To provide a fair assessment for the LLS, cheating has to be detected. Therefore, many research papers are concerned with how to detect cheating. One of the techniques used to detect cheating is based on the mouse dynamics of the student while conducting the experiment. In the mouse dynamic, it measures the mouse movement as a biometric. There are many other biometrics used, like fingerprint, face recognition, eye recognition, and others [5,6].  
The mouse dynamic is one of the best techniques as it does not require extra cost for equipment to collect the biometrics. Many techniques used mouse dynamics, which are intrusion detection [7], login system [8], user authentication [9], and others. Regarding the intrusion detection technique, it uses the collected mouse dynamics to extract from it several features, which is used to detect the impostor to systems. Furthermore, the login system is one of the techniques that use mouse dynamics, where the user is asked to perform a task, and from his mouse dynamics, the system decides whether to login or not to the system instead of using the regular username and password. Moreover, the user authentication technique is similar to the login system, where from the mouse cognitive style, the system can detect if this user is authorized to access the system or not. As a consequence, mouse dynamics have attracted many recent types of research [10, 11, 12, 13, 14]. On the other side, some research papers use the mouse keystroke rather than mouse dynamic, but always when dealing with laboratory experiments, mouse movement is more frequently used than keystrokes [15].