## 7 Conclusions  
The system proposed in this article achieved its objective as a test experiment was developed to simulate the student's programs, a correct program, and an incorrect program that verified whether the architecture of this system could help in the automatic correction of experiments in remote-access laboratories. This system was developed through layers--all layers communicated via a database. Nevertheless, this data exchange was essential so that the data from the experiments could be analyzed by algorithms that process the data acquired by the experiment layer and then processed by the didactic twin layer. The responses and support for these activities are in the management layer. In this work, a concept called the didactic twin was also created; this element is nothing more than each step of the experiment, plus the duration of the step and an image at the time of the step. With the didactic twin, it was possible to automatically correct student's experiments by taking the steps of the experiment and transforming these steps into an easier way for the generative AI to find the pattern of the experiments, even if the experiment had more than one solution in the processes of the mechatronics area.  
The system developed is a proposal that aims to evaluate the result of an experiment and the entire path that the student developed to reach the definitive answer. Therefore, this architecture is a tool that helps us correct the teacher's experiments automatically. The functioning of this architecture with three layers facilitates understanding, helps in the organization of the experiments, and contributes to the evaluation process in the remote laboratories of the mechatronics area. The digital twin of the teacher's experiment and the functioning of the student's experiment are compared to verify whether the correction is correct or incorrect. However, the teacher can still choose the most significant step of the correction to classify the experiment.  
This article's significant contribution was the development of a system capable of helping correct experiments and that avoids possible data in which the generative AI API finds a pattern; this pattern is the learning of the experiment and can always be used when the student is going to correct the experiments without the need for the teacher to be present to carry out the correction.  
Conceptualization, G.S.M.; methodology, G.S.M.; software, T.R.M.S. and G.S.M.; validation, G.S.M., T.R.M.S. and V.F.L.J.; formal analysis, I.V.B.; investigation, G.S.M. and I.V.B.; resources, V.F.L.J.; data curation, R.L.P.M.; writing--original draft preparation, G.S.M.; writing--review and editing, G.S.M., F.A.C.A.J. and V.F.L.J.; visualization, R.L.P.M.; supervision, V.F.L.J.; project administration, R.L.P.M. All authors have read and agreed to the published version of the manuscript.  
The National Council for Scientific and Technological Development (CNPq), the Federal Agency for Support and Evaluation of Graduate Education (CAPES), and FAPEAM (Fundacao de Amparo a Pesquisa do Estado do Amazonas) supported this research by providing financial resources through scholarships and fellowships. We sincerely thank FIT--Amazon Technology Institute for providing equipment and study hours, which were essential for the development of this work. This research, carried out within the scope of the Samsung UFAM Project for Education and Research (SUPER), according to Article 39 of Decree No. 10.521/2020 (SUFRAMA), was funded by Samsung Electronics of Amazonia Ltd.a., under the terms of Federal Law No. 8.387/1991, through agreement 001/2020, signed with the Federal University of Amazonas and FAEPI, Brazil.  
Not applicable.  
Data Availability Statement: Data from this research will be available upon request to the authors.  
This work was performed as an activity related to a doctoral thesis in the graduate program of PPCGEE-UFAM (Electrical Engineering Program of the Federal University of Amazonas--Brazil).  
The authors declare no conflicts of interest.