There are diverse applications of integrating AI with education e-learning process, the following points describe several practical applications:

• Personalized learning: is an educational approach that customizes education material to meet each student’s unique needs, interests, and learning styles. Teachers can implement personalized learning plans using Machine Learning-based methods like student choice in learning and self-paced learning. Recorded Data of the student is a golden resource for improvement. More informed decisions can be made about the curriculum by analyzing student data. Based on the results of analyzing individual learner needs, more personalized and effective educational content can be created. Data-Driven Decision making can be followed in assessment cases. For example, in case the student is graded highly most of the time, the system automatically increases the difficulty level of his own next quiz in aim to improve his capabilities. On the other hand, in case the student is graded lower most of the time, the system automatically recommends him related educational materials to improve his Data-Driven Decision making can be followed in assessment cases. For example, in case the student is graded highly most of the time, the system automatically increases the difficulty level of his own next quiz in aim to improve his capabilities. On the other hand, in case the student is graded lower most of the time, the system automatically recommends him related educational materials to improve his weaknesses. Therefore, this approach leads to better outcomes for students, teachers, and educational institutions.
• Sessions Scheduling: AI-based models can be utilized to schedule and organize tasks based on data provided by the company. This saves a lot of time and effort. The stakeholders need to save time and effort, with preserving the quality level of task management. Therefore, they can use machine learning-based models to predict schedules based on previous schedules, teacher availability, student availability, and learning goals. Machine Learning-based models can create and compare multiple scenarios to propose the optimal one for the learning goal.
• Exams generating: Exams usually consume time to prepare and grade. However, when the educational institute employs AI for the educational development process, the educational material can be uploaded, so the exam is created more quickly. The assessment process also usually consumes considerable time and effort, with a higher probability of error occurrence. AI-powered systems can complete the task in minutes with a lower probability of error occurrence.
• Plagiarism detection: Plagiarism is a major threat to academics and must be suitably addressed to ensure integrity and authenticity. According to the advancement in Natural language processing, the academic can rely on machine learning-models to observe plagiarism. We can utilize Natural language processing to detect plagiarism hidden within the articles based on complex algorithms and language models, which were trained on Semantic analysis, Lexical analysis, and Syntactic analysis.
• Smart chatbot for technical support: AI-powered systems that are designed to be available around the clock and answer student inquiries at any time in an automated and intelligent way that emulates human speech behavior and maintains the flow of the dialogue.