## 1 Introduction  
Currently, experimental activities and educational resources have undergone several transformations that play a fundamental role in the field of educational science [1, 2], especially in higher education in mechatronics where experiments in laboratories are an indispensable part of the course so that students can obtain effective skills and practical experience [3, 4, 5]. The use of laboratories is essential, but the use of these environments by students in educational institutions is becoming an obstacle as machines and equipment are not always available to keep up with the technological demands of students and the market and are limited in quantity and time [6, 7]. However, educational institutions, teachers, and students seek tools to help the teaching and learning process [8, 9]. One of the solutions is the use of remote laboratories with physical equipment because, with this type of access,it is possible to practice skills, improve experimental activities, and create environments using actual equipment and scenarios [10]. The fundamental concepts of remote laboratories are as follows: According to [PERSON] [11], a remote laboratory is a technology composed of software and hardware that provides a real learning experience with remote access via the Internet. [PERSON] et al. [12; 13] described remote labs as enabling students to evaluate and monitor the progress of experiments remotely, regardless of the availability of physical laboratories and equipment. Similarly, [PERSON] et al. [14] defined remote laboratories as software and hardware tools that allow students to access actual university equipment. [PERSON] et al. [15] also highlighted that remote labs serve as educational resources that extend beyond the capabilities of virtual laboratories. Moreover, [PERSON] and [PERSON] [16] emphasized that remote laboratories are natural laboratories where equipment and students are physically separated. In summary, remote laboratories are software and hardware tools that generate a real learning experience, granting remote access to physical experiments via the Internet [17; 18]. These tools offer several advantages, including equipment sharing between institutions [5; 19], flexible scheduling [20; 21], personalized learning experiences, and fostering self-discipline among students to complete tasks [22]. However, most remote laboratories also present challenges, such as a lack of standardization, environments that fail to provide flexibility in developing experiments [23; 24], and insufficient engagement and immersion between students and experiments [25; 26]. Remote laboratories play a crucial role in student education [27] as they facilitate learning experiences that involve high-value technological equipment and provide access to multiple resources, including experimental monitoring and correction. To enable the tracking of student experiments, this system employs the principles of digital twin technology. Furthermore, artificial intelligence algorithms will be integrated with the digital twins of the experiments to automate the correction process in the teaching and learning environment within remote laboratories. To understand digital twin (DT) technology, its definition is presented by several authors: [PERSON], [PERSON], and [PERSON] [28] described digital twins as a set of heterogeneous data processed by software tools. [PERSON] et al. [29] explained that digital twins capture real-world data across various disciplines, quantities, scales, and probabilities. [PERSON] and [PERSON] [30] defined the DT as a continuous connection between a real object or process and its digital counterpart, with data transferable in both directions. [PERSON] and [PERSON] [31] emphasized the concept of dynamic replicas of the real environment for achieving a DT. In contrast, [PERSON] et al. [32] highlighted DT as an emerging and transformative technology with significant potential for the future of industries and society.