## 1 Introduction
This concept is particularly relevant for integrating physical machines into the virtual world [33; 34], facilitating interactions and analyses in mechatronics applications [35], replicating and executing the behavior of experimental peripherals [36], emulating environments across various domains [26; 37; 31], and democratizing and enhancing learning processes in educational platforms [38; 39]. Artificial intelligence (AI) is an intelligent machine designed to perform tasks that typically require human intellect, including voice recognition, natural language processing, learning, and problem-solving capabilities [40]. With advancements in AI, particularly generative AI utilizing large language models (LLMs), applications have expanded across various domains. AI has significantly elevated technological support in education, fostering greater interactivity and innovation. It has assisted educators in managing their teaching processes more effectively while serving as an educational tool that offers students a wide array of learning opportunities [41; 42]. According to [PERSON], [PERSON], and [PERSON] [43], an LLM is an advanced machine learning tool that leverages text understanding and generation to assist in various tasks, particularly in the educational field. Nevertheless, within the education sector, applications involving images have benefited from computer vision combined with convolutional neural networks (CNNs), which aid in object detection in classrooms and laboratories and image classification for educational activities. In this work, digital twins and remote laboratories are the technological elements contributing to the evolution of the teaching and learning process in the field of mechatronics. However, the challenge of this proposal lies in how to perform the automatic correction of student's experiments in remote laboratories. Consider the following scenario: a student wants to verify whether their activity is functioning correctly. This typically depends on the professor's evaluation to check the performance of what the student has accomplished in the didactic experiment. However, what if the student is studying at 3 a.m.? Who will correct this activity? Or who will assist the student in solving the activity? Given this scenario, the fundamental idea was to utilize digital twin technology as a base tool capable of capturing data from the professor's experiment (Professor's digital twin) and the student's experiment (Student's digital twin) and then compare these data to perform automatic corrections. In this way, the student's experiment can be evaluated anytime. While digital twins facilitate data capture in remote laboratories, it will also be necessary to leverage convolutional neural networks (CNNs) and the GPT chat API to assist in correcting experiments by analyzing the images and data captured during the experiment's execution. A CNN is a neural network with convolutional, pooling, and fully connected layers designed for processing two-dimensional data. It is typically used for feature extraction, object detection, and image classification [44; 45; 46; 47]. Generative AI, with ChatGPT 4.0 being the most famous example, is a tool with the potential to enhance higher education by improving and facilitating educational experiences for both students and professors [48; 49; 50; 51]. Therefore, these two technologies assist in the correction of experiments in remote laboratories in the system of this work, given that technology-assisted correction has already proven to be helpful in distance education school management platforms (e-learning) and that it leaves a digital trail where it is possible to transform the data generated during experiments into information [52; 53]. The article is organized as follows: Section 2 presents related works on digital twins in remote laboratories and learning assessment systems. Section 3 outlines the project design. Section 4 details the components developed and implemented in the system.