## 5 Results and Tests  
The scenario created to evaluate this system followed the following steps:  
* 1 st Step:  
A teaching plant from the Industry 4.0 Laboratory of the Federal University of Amazonas called CP-LAB was used. This plant has six modules that form a teaching process for producing cell phones (Figure 4). Only the process of inserting the back cover of the cell phone was used to evaluate this system. Figure 5 shows a scenario called CPLAB_Magfront. This experiment uses a conveyor belt to carry the transport pallet, a parts storage unit, a mechanism to carry out production, and sensors to indicate the start and end of the conveyor belt process. The back cover is inserted into the parts storage unit to begin the cell phone assembly process. * 2 nd Step:  
The teacher registered a didactic plant in the system called CPLAB_Magfront through the management layer. This registration inserted the didactic plant's inputs and outputs, communication endpoint, and experiment. The teacher created the experiment Teste_magfront. Among the registered information, special attention must be paid to whether the laboratory endpoint and the inputs and outputs were inserted correctly for the proper functioning of the activity developed by the teacher. After registration, the teacher must execute the proposed experiment and click the learning experiment button. * 3 rd Step:  
At this point, the manager element application begins monitoring the experiment to capture the steps, checking the state changes of each registered input and output to save the execution steps. The time from the previous step to the next is recorded for each saved step in addition to activating the image capture application. This learning process forms the professor's experiment digital twin, and the information is stored in a.txt file and the database as a template for the professor's experiment for Test_magfront. * 4 th Step:  
Two test programs were developed as if they were student programs to test this system--one with the correct operating sequence and another with incorrect functionality. These programs were created in Ladder language using the TIA Portal V16 software. For these experiments, the OPC-UA communication protocol was used. After developing the programs, the Start My Experiment button was clicked, the tests were executed on the system experiment, and, shortly after, the Assess My Experiment button was clicked to evaluate the experiment. The test results will be presented according to the performance of both programs to verify if the system can learn the experiment pattern as proposed by the professor, forming the professor's experiment digital twin, and whether the system's evaluation of the student's experiment is accurate compared to the professor's experiment. Initially, it was checked whether the professor's experiment pattern was identified by two APIs--Gemini and ChatGPT. Both did not yield good results when the input and output values were directly provided to detect the operation pattern. Therefore, it was necessary to introduce the preprocessing element to convert the numbers into integers before applying them to the APIs. Using this methodology, no errors were detected in identifying the experiment pattern with the ChatGPT API as the GPT creation feature from the OpenAI platform was utilized, allowing the customization of a GPT using documents, images, and even rules. In this case, the following rules were implemented:  
* Request the number of inputs and outputs to determine the maximum expected amount. Return the main and most significant pattern of the input sequence. Provide the result as a vector without any additional text. Following these rules, the ChatGPT API correctly returned the experiment operation patterns. Figure 16 shows the ChatGPT interface with the implemented configuration rules. The Gemini API had difficulty identifying the experiment pattern as its responses consistently requested more information about the pattern's characteristics to be detected.