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 proper integration of technology in teaching and learning processes must consider the role of teachers and students, as well as the design of tasks and the context in which they are implemented. Teachers' perceived self-efficacy significantly influences their willingness to integrate educational robotics (ER) into their practice, so initial teacher training should provide opportunities for teachers to participate in structured activities that integrate ER. In this study, a class of pre-service teachers from an initial teacher training programme were provided with their first contact with an ER platform through the use of a simulator. We present the design process of a student exploration guide and teacher guide, developed over three iterative cycles of implementation, assessment and redesign. The analysis of the data collected allowed for improvements in the design of the tasks, the graphic component of the student exploration guide, and more precise indications for the teacher's actions. The main contribution of this study is the chain orchestration between the simulator, student exploration guide and teacher guide, which allowed pre-service teachers to solve a set of challenges of increasing complexity, thereby progressively decreasing their difficulties and contributing to an adequate integration of ER in their future teaching practices.

Abstract
The Internet revolution in 1990, followed by the data-driven and information revolution, has transformed the world as we know it. Nowadays, what seam to be 10 to 20 years ago, a science fiction idea (i.e., machines dominating the world) is seen as possible. This revolution also brought a need for new regulatory practices where user trust and artificial Intelligence (AI) discourse has a central role. This work aims to clarify some misconceptions about user trust in AI discourse and fight the tendency to design vulnerable interactions that lead to further breaches of trust, both real and perceived. Findings illustrate the lack of clarity in understanding user trust and its effects on computer science, especially in measuring user trust characteristics. It argues for clarifying those notions to avoid possible trust gaps and misinterpretations in AI adoption and appropriation.

Abstract
Augmented reality (AR) is a field of knowledge being developed since the middle of the last century. Its use has been spreading because of its usefulness, but more recently because of mobile platforms being widespread and accessible. AR has been applied in several fields of activity, and also in the field of Education and Training, because AR has several advantages over other teaching methods. In this paper, we search and analyze surveys and reviews of AR to present a brief history and its definition. We also present a classification of our sample under a scheme we developed in past work, and present also examples of technologies and applications of AR in each field. Finally, we do a deeper analysis over the publications of Education and Training, advantages and issues of AR in this field, and some research trends.