This Innovative Practice research paper describes an intelligent digital twin platform designed to enhance the virtualization of practical activities in the field of mechatronics education. In recent years, the classic concept of digital twins has significantly evolved in academia and industry. In education, especially in engineering, digital twins have brought countless alternatives for building environments close to the reality of students' practical activities. To improve these activities in the mechatronics area, intelligent digital twins will add added resources and technologies that assist the virtualization process. Therefore, a platform was developed to access remote laboratories in the mechatronics area and help correct the experiments offered. This platform will enable teachers to insert practical activities into remote laboratories on the platform. This platform has three central areas: teaching plans, teachers, and students. In teaching plants, it is possible to create a register of plants that provides information on the characteristics necessary to connect with their equipment and the number of plant elements. In the teachers' area, it is essential to create an activity for students to conduct, and this activity must be conducted for the system to capture the data and images to form a correction model. The correction model uses these data to help intelligent digital twins digitize practical activities. In the student area, it is possible to check all available experiments and their virtualizations and automatically correct the practical experiment.

This innovative practice WIP is about using serious games in robotics education. In the realm of STEM education, the professional development of students is intrinsically linked to practical experiences, such as hands-on exercises, experiments, and laboratory classes. However, the financial implications of acquiring and maintaining mechatronic devices such as robots, automation-related equipment, and instruments for these activities are high. These excessive costs, which cover initial funding and ongoing maintenance, pose challenges for educational institutions in establishing and maintaining well-equipped laboratories. In addition, access to these resources depends on their availability, creating restrictions on student learning opportunities. The state of Amazonas, Brazil, needs to solve these problems; as a city of numerous factories, there is a growing demand for well-trained professionals and a dwindling number of resources to apply to education. To address these challenges, we propose an approach that integrates simulation tools for application in educational games. These tools used to conduct simulations are essential for developing shop floor scenarios without physical equipment, effectively reducing the costs associated with mechanical collisions and problems related to student programming errors. The importance of using simulation tools in the learning process lies in their ability to offer a risk-free environment where students can experiment, learn, and perfect their skills without incurring additional expenses for corrective maintenance. Innovatively, the incorporation of gamification-based learning by simulating real problems based on the shop floor is gaining momentum. We believe this is the most attractive way for trainees or students to deliver fast, high-quality professionalism. This approach represents an appealing alternative for educators looking to enhance their educational experience. From this perspective, this paper presents the application of a serious game in the industrial laboratory at UFAM. The aim is to offer students a dynamic and engaging platform for learning about collaborative robots, taking advantage of the principles of the learning factory, and promoting good practices in an economical and risk-controlled virtual environment.

This research-to-practice full paper focuses on the use of technology in education, which enables a wide variety of activities. For example, a computer with internet access allows students to read articles, make video calls, perform group work, and even take tests. In 2021, this tool was relevant to face-to-face teaching because the interruption caused by COVID-19 resulted in social distancing. In this way, remote teaching was adopted as an emergency measure in the teaching process. Although remote teaching uses technological innovation, it lacks the inclusion of educational theories and remote practices in virtual classes. Thus, this approach analyzes Lev Vygotsky's theories of learning, specifically the zone of proximal development and scaffolding, to understand the satisfaction of learning the content alone or with other people's help through the Industrial Internet of Things (IIoT) technology. It then investigated IIoT, intending to build a remote virtual environment. In this context, it was possible to develop a remote teaching method called Hobots. This remote teaching method comprises six collaborative modules (the integrating platform, interfaced, MQTT broker, communication module, server, and Robot-X) for the remote teaching of industrial robotics. In addition, the method was evaluated by ten students using the ISO 9241 - Part 10 - Dialog Systems standard, with a view to their satisfaction with using the method in the remote teaching process, and the results were admissible. Therefore, in addition to theory and simulation, the proposed method stands out for the possibility of remote implementation.

This research-to-practice full paper presents a platform developed to support AI teaching, training, and evaluation. Artificial intelligence (AI) has revolutionized several fields, including pattern recognition, natural language processing, and computer vision. In this context, convolutional neural networks (CNNs) have been effectively utilized to detect objects in images. In industry, AI has the potential to optimize processes and increase efficiency, becoming an essential factor in the evolution of manufacturing systems. However, the training of AI models is complex and requires a sophisticated level of knowledge for data manipulation and an understanding of model training. To simplify this process, a web-based platform has been proposed to facilitate the training of YOLO networks. The platform was developed via the Streamlit library, which provides an interface to guide users through model training. The platform aims to lead users through six steps to obtain and deploy a model directly through the platform, enabling performance evaluation and metric tracking.

The COVID-19 pandemic has imposed numerous restrictions on face-to-face meetings, and one of the most impacted activities was engineering education. Most teaching professionals have adopted diverse solutions based on distance learning techniques. However, practical experiments that require laboratories could not be performed in most cases. One of the subjects studied in modern engineering that has been impacted is related to industrial automation systems. In April 2020, amidst tighter restrictions on social contact, we started cooperating with a local company to train our students on topics of interest to the corporation. Our research group was responsible for dealing with Industry 4.0 and its associated technologies. Over the past two years, we have conducted this interaction with the industry under the pandemic's constraints and obtained very interesting results. In this report, on an innovative experience in engineering education, we will describe our project’s syllabus, how the relationship was between students and teachers through online tools, and mainly how we managed to ensure that students had access to experiments close to the industrial reality.

Remote Access Labs (RAL) are hardware and software tools that allow students to remotely operate actual equipment in physical labs at schools or universities. This work presents the development of a system for students to use remote access laboratories in the mechatronics area without human supervision. This system verifies the activities the registered labs can offer through algorithms that use digital twin technology to learn the actual or virtual industrial plants’ operations. Registered laboratories are available for the practice of experiments where students can automatically follow the correction of activities through virtualization generated so that the student can understand their actions. With this system, students can monitor their learning in real time and receive evidence of successes and errors through gamification of their performance.

Much has been discussed recently about the implications and strategies adopted by higher education institutions concerning the realization of online practical classes due to COVID-19. Some teaching institutions used virtual meetings to reorganize the lesson plan to continue teaching and assessing the students due to the suspension of face-to-face classes due to this epidemic. Although the most adopted strategy among the courses has been direct communication between students and professors through e-mail, telephone, social networks, and message apps, education lives in a time when transitions seem to occur much faster than in recent decades. For example, online engineering courses, specifically robotics, face difficulty implementing practical activities online because the educational tools available remotely are scarce or very expensive, thus becoming an obstacle to circumvent. Given this, this article presents a methodological strategy for the teaching-learning practices of engineering through Learning-IoT. This is a methodology for teaching robotics using the Internet of Industrial Things (IIoT) concepts. It enables the connection of the student to a Physical Objects Framework proposed in practical online activities and by the project method responsible for stimulating critical thinking. Thus, the initial ergonomic interface test experiments and usability of the proposed methodology demonstrated in the experiments in online practical classes that memorization and decision-making fit new possibilities or functionalities.

This Research to Practice Work in Progress presents a solution for managing distance learning laboratories. Remote Access Laboratories are software and hardware tools that allow students to remotely operate real equipment located in physical facilities such as universities laboratories. This work presents the development of an Automatic Selection System for Remote Access of Mechatronic Laboratories. The system checks the current laboratory activities in real time, verifying if it is active, inactive, occupied or free, according to a student’s experiment. After analyzing those characteristics, the system examines the number of students and the state of the internet connection to determine the most appropriate laboratory. Finally, experiments using the selection functions and the proposed architecture are presented to investigate the system’s efficiency.