## 2 Related Works  
ITSs are typically classified into several different parts, and each part plays an important role. The basic architecture of an ITS consists of four components: student model, knowledge domain model, tutoring model, and user interface model [11; 21]. These basic components interact with each other to achieve different functions. Several ITSs have been developed for learners in different areas with various strategies in order to improve teaching ways and help students learn better [8; 22]. ITSs aim to adapt a comprehensive learning approach to meet the needs of students. Therefore, it is essential that the students' model should be created accurately while considering their knowledge levels, learning skills, and preferences [22; 23]. Then, the information required must be used and developed in order to improve the e-learning environments. Hence, AI techniques are regarded as useful tools for several reasons as they have the ability of developing the human decision-making process and building automatic learning models. There are several AI techniques that have been used to build and develop intelligent e-learning systems, including fuzzy logic, Bayesian networks, neural networks, and genetic algorithms. These techniques can help one to learn from uncertain, vague, and incomplete data. Furthermore, the ability of AI techniques to imitate the intelligence of human beings, and their ability to solve complex problems, makes them ideal tools in e-learning. Artificial Neural Network (ANN) is a supervised learning model consisting of a large number of simple processing units, called \"neurons\", arranged in different layers, known as input layer, hidden layer, and output layer, while a multilayer neural network is a connection of simple neurons called \"perceptron\". Neural networks can be utilized to develop intelligent e-learning systems to assist during the educational process and act as the teacher in the traditional classroom. Learning Styles Identify Artificial Neural Networks (LSID-ANN), an approach based on artificial neural networks, is used by [PERSON] et al. [24] for identifying students' learning styles, based on the Felder-Silverman learning style model. LSID-ANN uses four artificial neural networks with a three-layer perceptron configuration. Each network is designed for one of the four learning style dimensions: active/reflective, sensing/intuitive, visual/verbal, and sequential/global. In addition, the authors evaluate the LSID-ANN approach by using real data from 127 computer science undergraduate students, including their behavior data in a university course and their results on the Index of Learning Styles (ILS) questionnaire. This study achieved a pleasing accuracy level of learning style identification; thus, helping teachers with giving good advice to their students, as well as increasing student performance and learning satisfaction. Genetic algorithms are an evolutionary computing method of artificial intelligence used to solve complex problems because they provide a large number of approximate alternative solutions for optimal solutions. The idea of genetic algorithms is based on the mechanism of natural selection and the natural gene system [25]. [PERSON] et al. [26] studied the problems facing the development of e-learning systems as an optimization problem and addressed them using genetic algorithms. They described an adaptive system used to generate adaptive pedagogical paths based on the learners' profile and current basic learning objectives. A genetic algorithm is successfully applied by [PERSON] [27] to evaluate a personalized learning system able to dynamically update the process of the course and the target user model during the learning process. As a result, this study provides a good framework with the ability to generate the personalized courses in an e-learning environment. Fuzzy logic and Bayesian network techniques are widely used for developing intelligent educational systems.