Abstract

Abstract
Este estudo tem como objetivo discutir o potencial das abordagens de Educação OnLIFE e de Ambientes Virtuais Imersivos na transformação do ensino-aprendizagem em engenharia, focando especialmente em superar os principais desafios contemporâneos da área, tais como a falta de experiência prática, a obsolescência das infraestruturas de laboratório e a inadequação dos métodos de ensino convencionais. Com base na fundamentação teórica, o estudo explorou como a Educação OnLIFE e a Aprendizagem Imersiva podem contribuir nos processos de ensino-aprendizagem da engenharia, proporcionando uma experiência educacional mais adaptativa e experiencial. A análise empírica do estudo apresentou exemplos específicos onde estas metodologias podem ser aplicadas para criar ambientes de aprendizado que simulam experiências industriais reais e projetos de engenharia, destacando o potencial desta abordagem para melhoria da interatividade e na personalização da aprendizagem. As conclusões reiteram que, ao enfrentar os problemas identificados, a adoção de Educação OnLIFE combinada com estratégias de aprendizagem imersiva tem potencial para enriquecer o currículo de ensino de engenharia, para desenvolver tanto a competência técnica quanto atitudinal dos graduandos para os desafios contemporâneos do mercado de trabalho.

Abstract
The co-creation of games is a research area that has shown very promising results in identifying technological requirements. It is an approach where the researcher usually adopts the role of a participant observer, guiding the dynamics of co-creation acts. This situation limits the opportunities for replicability of co-creation methods by independent facilitators, which could elucidate the quality and improvement opportunities of these methods, contributing to their more widespread application. In this paper, we present a methodology that aims to overcome this limitation, allowing the replication of co-creation workshops by different independent facilitators. This methodology was conceived in the context of collecting relevant information for the design of an educational digital platform that intends to use gamified resources for adult education in digital health data literacy. Specifically, co-creation workshops were used to gain an overview of the difficulties of different age groups in this area and their perspective on which games would best address these difficulties. The workshops were conducted in five countries with planning oriented so that each country could have a different facilitator, not requiring the presence of the researcher who designed them. The challenge of this planning was to maintain the approach of the facilitators identical in all countries, as best one could. We present here the method adopted through its planning and materials designed for information collection, which included brainstorming using card sorting and game ideation with the use of templates. The analysis of replicability by independent facilitators was done by scrutinizing the produced elements, which allowed us to observe the aspects of coherence and divergence among the various facilitators. Thus, we conclude that this approach is a good starting point to overcome current limitations and identify possible lines of improvement. © 2024 Copyright held by the owner/author(s).

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.