Abstract
Existe uma ampla variedade de ferramentas e ambientes disponíveis para aplicações de realidade virtual imersiva, passíveis de utilização em contexto educativo. Para proporcionar uma perceção panorâmica das potencialidades disponíveis, este estudo efetuou um levantamento e categorização dessas ferramentas educativas, classificando-as por áreas de aplicação: exploração geográfica, entretenimento, ciência, arte e outras. Recorreu-se metodologicamente ao protocolo de levantamento (scoping review) proposto por Morgado & Beck. Com base neste protocolo efetuaram-se os processos de definição e desenvolvimento das buscas, da seleção e análise de elementos e extração das conclusões. As ferramentas foram também analisadas face à tipologia de usos de ambientes imersivos dos mesmos autores, segundo a qual constatámos que o tipo de ferramentas mais prevalente é o referente a “Manipulação Interativa e Exploração”, seguido pelas de “Interação Multimodal” e “Treino de Competências”. São também comuns as ferramentas de Colaboração. Algumas categorias menos prevalentes, como “Ver o Invisível”, “Envolvimento”, “Simulação do Mundo Físico” e outras, permitem ainda assim ter uma perceção de como se concretizam essas tipologias de usos enquanto experiências de aprendizagem possíveis em ambientes virtuais imersivos.

Abstract

Abstract
The proposed framework (Human-Centered Trustworthy Framework) provides a novel human-computer interaction approach to incorporate positive and meaningful trustful user experiences in the system design process. It helps to illustrate potential users' trust concerns in artificial intelligence and guides nonexperts to avoid designing vulnerable interactions that lead to breaches of trust.

Abstract
Learning environments unquestionably enable learners to develop their pedagogical and scientific processes efficiently and effectively. Thus, considering the impossibility of not having conditions of autonomy over the routine underlying the studies and, consequently, not having guarantees of the learning carried out makes the learners experience gaps in the domain of materials adequate to their actual needs. The paper's objective is to present the relevance of the applicability of Artificial Intelligence in Recommendation Systems, reinforced through the Assurance of Learning, oriented towards adaptive-personalized practice in corporate e-learning contexts. The research methodology underlying the work fell on Design Science Research, as it is considered adequate to support the research, given the need to carry out the design phases, development, construction, evaluation, validation of the artefact and, finally, communication of the results. The main results instigate the development of an Adaptive-Personalized Learning framework for corporate e-learning, provided with models of Artificial Intelligence and guided using the Assurance of Learning process. It becomes central that learners can enjoy adequate academic development. In this sense, the framework has an implicit structure that promotes the definition of personalized attributes, which involves recommendations and customizations of content per profile, including training content that will be suggested and learning activity content that will be continuously monitored, given the specific needs of learners.

Abstract
The manual monitoring of refilling stations in industrial environments can lead to inefficiencies and errors, which can impact the overall performance of the production line. In this paper, we present an unsupervised detection pipeline for identifying refilling stations in industrial environments. The proposed pipeline uses a combination of image processing, pattern recognition, and deep learning techniques to detect refilling stations in visual data. We evaluate our method on a set of industrial images, and the findings demonstrate that the pipeline is reliable at detecting refilling stations. Furthermore, the proposed pipeline can automate the monitoring of refilling stations, eliminating the need for manual monitoring and thus improving industrial operations’ efficiency and responsiveness. This method is a versatile solution that can be applied to different industrial contexts without the need for labeled data or prior knowledge about the location of refilling stations.

Abstract
Artificial intelligence (AI) has gained significant evolution in recent years that, if properly harnessed, may meet or exceed expectations in a wide range of application fields. However, because Machine Learning (ML) models have a black-box structure, end users frequently seek explanations for the predictions made by these learning models. Through tools, approaches, and algorithms, Explainable Artificial Intelligence (XAI) gives descriptions of black-box models to better understand the models’ behaviour and underlying decision-making mechanisms. The AI development in companies enables them to participate in Industry 4.0. The need to inform users of transparent algorithms has given rise to the research field of XAI. This paper provides a brief overview and introduction to the subject of XAI while highlighting why this topic is generating more and more attention in many sectors, such as industry.