Introduction: This review paper provides a comprehensive examination of the applications and impact of artificial intelligence (AI) in the field of education. With advancements in AI technologies, the educational landscape has witnessed significant transformations. This review aims to explore the diverse AI techniques employed in education and their potential contributions to teaching, learning, assessment, and educational support.Objective: This research article aims to tracing the development of AI in education from its early beginnings to its current state. It highlights key milestones and breakthroughs that have shaped the field, including the emergence of intelligent tutoring systems and expert systems.Methods: The article provides a comprehensive overview of the various AI techniques utilized in education, such as machine learning, natural language processing, computer vision, and data mining. Each technique is discussed in detail, showcasing the algorithms, models, and methodologies used within each approach.Results: While the benefits of AI in education are substantial, the paper also addresses the challenges associated with its integration. Ethical considerations, privacy concerns, and the need for effective human-AI collaboration are discussed in-depth.Conclusion: this review underscores the transformative potential of AI in education. By harnessing AI technologies effectively and responsibly, educators and policymakers can unlock new possibilities for enhancing teaching and learning experiences, fostering personalized instruction, and driving educational advancement.

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The Covid-19 pandemic is affecting many aspects of society, especially university educational programs worldwide. As a result, online learning is an effective strategy that is adopted by many educational institutions nowadays. However, not all training institutions have the necessary environment, assets, and ability to conduct effective online learning, particularly in poor nations with resource constraints. As a result, many institutions are struggling to build traditional courses or E-Learning in limited condition while still meeting students' demands. To overcome this limitation, we present a technique for assessing the impact of these elements on the e-learning system. Then, utilizing data from students who have participated in the program, this is an issue of explaining the significance and prioritizing construction investments for every component based on the K-means clustering algorithm. The purpose of this paper is to investigate the relationship between the students' responses to e-learning platforms and their performance in terms of various skill levels with the help of K-Means clustering algorithms. The clustering findings demonstrate that individuals with greater levels of involvement outperform those with intermediate or lower levels of engagement.

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