Abstract
In this paper we present findings from a bibliometric evaluation of scientific publications on human-AI systems, indexed in the Dimensions database over the past five years (2018 to 2022). The study maps the research landscape in this burgeoning area, as it relates to the topic of collaboration. To this end, we assessed publication and citation counts over time, authorship-level indicators, and keyword occurrence frequency. We also examined funding information as an indicator of research priorities, alongside usage-based statistics and alternative metrics such as social media mentions, recommendations, and reads. Our preliminary findings highlight a significant focus on aspects like trust, explainability, transparency, and autonomy in highly complex scenarios through the use of generative models and hybrid interaction techniques. The results also reveal a growth in the number of publications and funding grants, although a certain lack of maturity is observable in terms of citation patterns and coherence of thematic clusters. © 2022 IEEE.

Abstract
The main objective of continuing education for trainers is to promote the updating, improvement, and acquisition of new didactic and pedagogical skills that cover different fields of action, namely the design, development, and implementation of training programs in the field of research and experimentation of new approaches and methodologies applied to diversified audiences and contexts, especially in e-Learning and b-Learning environments. To fulfill these competencies, the IEFP National Centre for Trainer Qualification (CNQF), besides managing and coordinating the training and certification system for trainers in Portugal, has been developing a modular structure for the Initial Pedagogical Training of Trainers and the Continuous Pedagogical Training of the Distance Trainer (e-Trainer) to contribute to the acquisition and development of pedagogical and technical competences of trainers that will contribute to raising the standards of quality of the training provided. Technological innovation and evolution launch new challenges to Trainers requiring a great effort to adapt and master both from the point of view of pedagogical models and communication processes in learning environments and digital content. This new Continuous Pedagogical Training Referential in Digital Content for Self-Learning (e-Content) was designed in this context. It explores the pedagogical and technological dimensions of producing digital content for distance learning environments. This article presents the fundamentals of this framework, its application, and validation in a case study supported by two e-Content training courses. With this case study and in a perspective of continuous improvement, we intend to understand how the modular structure of the adopted framework influences the results obtained by the trainees of the e-Content training courses and their degree of satisfaction.

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
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