Abstract
This chapter examines how Artificial Intelligence (AI) and Learning Analytics (LA) are transformingdistanceeducation, accelerated by the COVID-19 shift toe-learning. By using data from Learning Management Systems (LMS), these technologies can personalize learning, improve student retention, and automate tasks. AI, particularly machine learning, enables dynamic adaptation to student needs, while LA provides valuable insights for informed instructional decisions. However, ethical concerns, including data privacy and algorithmic bias, must be addressed to ensure equitable access and fair learning outcomes. The future of distance learning lies in responsible integration of AI and LA, creating immersive and inclusive educational experiences. © 2025 by IGI Global Scientific Publishing. All rights reserved.

Abstract
The ever-changing landscape of distance learning AI and learning analytics transforms engagement and efficiency in education. AI systems analyze behavior and performance data to provide real-time feedback for improved outcomes. Learning analytics further help educators to identify at-risk students while fostering better teaching strategies. By integrating AI with learning analytics, distance education becomes more inclusive, ensuring learners receive the support necessary to thrive in an increasingly digital and knowledge-driven world. AI and Learning Analytics in Distance Learning explores the development of distance learning. It examines the challenges of using these systems and integrating them with distance learning. The book covers topics such as AI, distance learning technology, and management systems, and is an excellent resource for academicians, educators, researchers, computer engineers, and data scientists. © 2025 by IGI Global Scientific Publishing. All rights reserved.

Abstract
Artificial intelligence (AI) has been developing, and its application is spreading at a good pace in recent years, so much so that AI has become part of everyday life in various sectors. According to several international reports, AI in Education is one of the emerging fields of technology in the education sector, from where much research is being developed to support educational processes. This paper aims to provide an overview of the research on AI applications in education management systems (LMS) in higher education through a systematic literature review following the protocol proposed by Kitchenham [1]. Three hundred six papers were initially identified from Scopus and EBSCOhost databases from 2010 to 2022, from which 33 papers were selected for final analysis according to the defined inclusion and exclusion criteria. The research results show that the LMS most used for implementing AI solutions in education is Moodle and that AI has been most used for student performance assessment based on student data. Among the AI algorithms used, Random Forest, Neural Networks, K-means, Naive Bayes, Support Vector Machine, and decision trees stand out. © 2022 IEEE Computer Society. All rights reserved.

Abstract
Although implementing technologies is a continuous practice observed in organisations, many need help to achieve successful implementations and recognise its impact on their operations and outcomes. Therefore, this review paper aims to present the critical success factors that organisations consider when implementing technology in the Talent Management field. A comprehensive understanding of the technological implementation phenomenon requires adopting a strategic perspective. Consequently, this literature review centres on three clusters: challenges organisations are addressing (Challenges), the technological capabilities and the implementation/adoption process (Technology) and the expected impact (Impact). Findings indicate that a central area of research is the integration of technology in recruitment and, particularly, in the context of Small and Medium Enterprises. Digital Transformation, the Industrial Revolution, and a more diverse workforce are challenges that organisations face. Organisations aim to streamline Human Resources Management (HRM) practices, prioritising data-driven decisions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Many organizations' performance and survival challenges need dynamic capabilities and technology to speed the development of those capabilities. Companies are constantly visiting the strategies used in learning as a crucial element in preparing their workforce for the accelerated changes. Learning Technologies stand as a facilitator of these challenges, which is why they are so important. There is still a good margin of exploration in the field of the learning technologies. The reality is that a reduced number of studies explore the technology as important in accelerating innovation, performance, and competitiveness. The present research will focus on the strategic implementation of learning technologies. The approach we chose to solve this problem is to develop guidelines that support the strategy for implementing technology in the learning field. The approach will allow us to relate the strategy with the challenges and the impact the organization is expected to achieve.

Abstract
This position paper provides an overview of the most important practices in the field of Artificial Intelligence (AI) used in educational contexts, with a focus on the main platforms used for teaching (LMS) to support the development of a research work at EduardoMondlane University (UEM) in Mozambique. To that end, definitions and descriptions of relevant terms, a brief historical overview of Artificial Intelligence (AI) in education and an overview of the common goals and practices of using computational methods in educational contexts are provided. The state of the art regarding the adaptation and use of Artificial Intelligence is presented and we discuss the potential benefits and the open challenges. The paper also presents the methodology and key steps which will be developed at UEM to achieve the research goals.