For over two decades, e-learning has been recognized as a flexible and faster method compared to the other established methods, especially in enhancing knowledge. Concurrently, the expansion of information technology applications, such as mobile applications and Artificial Intelligence (AI), has provided well-grounded foundations for e-learning to be more reachable. In particular, education can be seen as the most beneficial sector of advancements in e-learning. Machine learning is considered a form of personalized learning that could be used to give each student a specific personal experience through which students are directed to gain their own experience. Web and AI-enabled mobile applications can be recognized as one of the most broadly used platforms for e-learning where machine learning technology can be applied to measure many influences and predictions regarding the quality of e-learning, but we cannot ignore the complexities of use. This study shows the role of machine learning in the user’s ability to make use of the course and its contents to measure ease and clarity. Based on a former study shown previously, this paper attempts to pinpoint realities and complexities associated with web and AI-enabled mobile applications by evaluating user preferences. This paper forms the second phase using two user groups (21–30 years) where data were attained using a survey questionnaire to investigate the user preferences when using an application for e-learning. The analysis shows that the future of e-learning has greater potential in web-based applications, as they have more scope for development and improvements compared to mobile applications. The paper concludes with a conceptual framework that works as a machine that stimulates different information and uses e-learning applications that support artificial intelligence techniques. This research provides a solid underpinning for further research into the future of AI-enabled e-learning education and its implication with respect to cost, quality, and usability.

This research paper examines the transformative impact of artificial intelligence (AI) and machine learning (ML) on financial markets. It explores how these technologies are revolutionizing trading strategies, risk management, and financial operations. The paper reviews existing literature to understand the applications of AI and ML in finance. It then delves into potential research methodologies for analyzing the impact of these technologies on market efficiency, volatility, and investor behavior. Finally, the paper discusses the potential outcomes and challenges associated with the increasing adoption of AI and ML in financial markets.

Abstract: Artificial intelligence (AI) is a force that is transforming industries and reshaping societal norms in a variety of fields. This study paper offers a thorough analysis of the most current developments in artificial intelligence (AI) technology, their uses, and the moral issues raised by their widespread use. The first section of the paper explains the basic ideas and techniques of artificial intelligence (AI), such as machine learning, deep learning, and natural language processing. It explores how AI has developed from rule-based systems to more complex models like neural networks and reinforcement learning, emphasizing the turning points that have brought AI to this point. The study then delves into the various uses of AI in industries including healthcare, banking, transportation, and education. It looks at how AI-powered systems are boosting user experiences, streamlining decision-making procedures, and optimizing operations across a range of industries. The revolutionary effects of AI on several industries and society at large are demonstrated through case studies and real-world examples.

Technology movement creates progressive impacts towards the development of communication network, computer, and the Internet. An innovation that is known as Artificial Intelligence (AI,) is one of many products. The usage of AI technology is predicted to expand globally, including in Indonesia. The influence of AI technology on improving the effectiveness of e-Learning concepts enables integration into the advancement of the education system in Indonesia. This study aims to explore the potential and impact of applying AI technology in e-Learning, and the readiness of various factors that influence the education system and Information and Communication Technology (ICT) in Indonesia. The discussion is based on secondary data and comprehensive observations of the prevailing learning structure.

Artificial intelligence (AI)-based applications have found widespread applications in many fields of science, technology, and medicine. The use of enhanced computing power of machines in clinical medicine and diagnostics has been under exploration since the 1960s. More recently, with the advent of advances in computing, algorithms enabling machine learning, especially deep learning networks that mimic the human brain in function, there has been renewed interest to use them in clinical medicine. In cardiovascular medicine, AI-based systems have found new applications in cardiovascular imaging, cardiovascular risk prediction, and newer drug targets. This article aims to describe different AI applications including machine learning and deep learning and their applications in cardiovascular medicine. AI-based applications have enhanced our understanding of different phenotypes of heart failure and congenital heart disease. These applications have led to newer treatment strategies for different types of cardiovascular diseases, newer approach to cardiovascular drug therapy and postmarketing survey of prescription drugs. However, there are several challenges in the clinical use of AI-based applications and interpretation of the results including data privacy, poorly selected/outdated data, selection bias, and unintentional continuance of historical biases/stereotypes in the data which can lead to erroneous conclusions. Still, AI is a transformative technology and has immense potential in health care.

Artificial Intelligence (AI) assisted educational institutions extensively utilize electronic learning context to guarantee improved teaching and learning experiences accompanied by educational activities. E-learning or online learning plays a significant role in Chinese higher education. There is a challenge to implement e-learning in China’s higher education to improve course resources, student learning style prediction, teaching quality, and service support. Hence in this paper, Artificial Intelligence based Efficient E-learning Framework (AI-EELF) has been proposed to overcome the challenges faced by China’s higher education while implementing e-learning modules. The collected student data can be efficiently utilized and exploited to progress in an adaptive learning environment. The proposed AI-EELF method introduces multiple learning models to enhance teaching quality and predict the student learning style. The experimental results show that the proposed AI-EELF achieves high performance, prediction ratio in determining students’ learning style and improves teaching quality compared to other existing methods.

The integration of Artificial Intelligence (AI) and technology in English Language Learning (ELL) is revolutionizing education. This research explores the impact of AI and technology on English language learners at Quest University, Nawabshah. Through a quantitative approach and an online questionnaire, the Researcher delves into students' perceptions and experiences. Technology, encompassing computers, smartphones, tablets, and AI-powered chat bots, plays a pivotal role. The majority of students find technology-enhanced learning engaging and enjoyable, making it easier to practice English outside the classroom. AI-powered tools receive acclaim for increasing motivation, proficiency, and preference over traditional methods. However, the enjoyment of AI chat bots remains diverse.

Artificial intelligence (AI) is a helpful technology. By providing college students with more individualized and productive learning environments, they support education. This research examines how artificial intelligence (AI) works in education and enhances teaching and learning results for Mindoro State University students and teachers. The study will survey students and instructors to gauge their perspectives on AI, assess its benefits and obstacles in education, and propose strategies for successfully implementing AI into teaching and learning techniques. The poll will ask students about their understanding of AI and their experiences using AI-powered learning tools. According to the results of this study, students and teachers at the University have a strong belief and a positive outlook on the potential of AI to significantly improve the learning journey and outcomes for both students and instructors, as evidenced by the weighted mean rating of 4.37 for all categories combined. Overall, respondents strongly agree that artificial intelligence has the potential to considerably improve student and instructor learning and teaching outcomes at the University, as indicated by the average mean score of 4.23, which is characterized as "strongly agree." The study's findings will provide educators and policymakers with critical insights into the proper and successful use of artificial intelligence (AI) to improve student learning outcomes.

The utilisation of Artificial Intelligence (AI) technology has caused remarkable changes that have taken place in the educational landscape. Through the integration of AI in online learning systems, an entirely new educational experience has been introduced, altering the ways learners and educators can interact. The emergence and evolution of AI technology have increased efficiency and productivity, enhancing teaching and learning outcomes. AI in online learning provides a distinct advantage by providing real-time feedback to learners. Traditional learning environments often suffer from the limitation of delayed feedback, impeding learners’ progress and demotivating them. However, AI-powered online learning systems excel in delivering immediate feedback to learners, enabling them to promptly identify and rectify mistakes and enhance their performance in real-time. This timely feedback fosters a supportive learning environment that encourages learners to engage in the learning process actively. The research by Vanlehn, Lynch, Schulze, Shapiro, Shelby, Taylor et al. (2005) on the Andes physics tutoring system serves as a valuable resource for understanding the lessons learned from utilising AI to support learner-instructor interaction. In contrast to traditional learning environments that offer delayed feedback, impeding the progress of learners and possibly dampening their motivation, AI-powered online learning systems provide real-time feedback. With real-time feedback, learners can instantly correct mistakes and improve their performance, thereby advancing their learning outcomes (Zhou & Mei, 2021). This literature review explores the impact of AI on learner-instructor interaction in online learning environments. The review considers how AI technology enhances and diversifies the learning process, focusing on personalised learning, real-time feedback provision, and content delivery.

This review examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in addressing the complexities of systems engineering. It highlights how AI and ML are revolutionizing system design, integration, and lifecycle management by enabling automated design optimization, predictive maintenance, and efficient configuration management. These technologies allow for the analysis of large datasets to predict system failures and optimize performance, thereby enhancing the reliability and sustainability of engineering systems. Despite the promising applications, the integration of AI into systems engineering presents challenges, including technical hurdles, ethical considerations, and the need for comprehensive education and training. The paper emphasizes the importance of interdisciplinary approaches and the continuous evolution of educational programs to equip engineers with the skills to leverage AI effectively. Concluding thoughts underscore AI's potential to redefine systems engineering, advocating for a balanced approach that addresses both the opportunities and challenges presented by AI advancements.
 Keywords: Artificial Intelligence, Machine Learning, Systems Engineering, Automated Design, Predictive Maintenance, Configuration Management, Education and Training, Technology Integration.