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.

Abstract

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
The Internet of Things (IoT) emerged to describe a network of connected things on a large scale to offer services to a large number of applications in different environments and domains. Middleware is software that seeks to facilitate the management and communication of all these things, providing the necessary functionalities to manage things, to discover, to compose services, and perform communication. For this reason, several proposals for middleware solutions for IoT have been developed. In this article, we conducted a systematic review of the literature to bring together middleware solutions for IoT, identifying the requirements and communication protocols used. In addition, we present some gaps and directions for future research in the development of IoT middleware.

Abstract
The preservation and sustainability of the marine ecosystem could benefit from the surge of new technologies to design autonomous vehicles. These underwater robots operate in a complex environment where the loss of human lives is highly probable. Consequently, a considerable percentage of the ocean remains unexplored due to the complexities of the underwater environment. Robotics can be a solution to overcome these limitations. However, training these complex systems is challenging and resource expensive. Human-in-the-loop input is essential in accelerating the training process by teaching the robots how to perform in specific scenarios and validate the simulated environment. This work presents a case study that simulates the dynamics of a Remotely Operated Vehicle in an underwater environment and uses imitation learning to train the vehicle to navigate autonomously toward a target. It was possible to measure and observe the similarity between the expert and the autonomous trajectories generated by the ROV. However, the imitation learning performance cannot surpass the expert, considering the time and the number of successes in finding the target. © ESM 2022. All rights reserved.