## 5 Discussion
For this purpose, the following partner universities will be involved: Group 1 includes the University of Seville (Spain) and Haaga-Helia University of Applied Sciences (Finland), with which we are part of the Ulyseus European University consortium; Group 2 consists of long-term project partners, namely, the Silesian University of Technology (Poland), the University of Pardubice (Czech Republic), Obuda University (Hungary), and the University of Public Service (Hungary); Group 3 includes universities from the PEGASUS Aerospace Engineering Universities Association. In addition to measuring reaction time and error rate, additional variables, such as student stress levels, can be integrated into the technology to improve its adaptability. Also, to improve the generalizability of the model, cross-validation should be carried out by testing the model on data from different institutions. Using machine learning methods to process data from different sources will allow the creation of a multifactorial model that takes into account differences between institutions. It is also important to develop a long-term strategy for data collection and for updating the model, which will contribute to its high efficiency and adaptation to changes in educational processes. The rationality of the developed artificial intelligence technology has been proven experimentally. An adequate and well-founded mathematical apparatus provides the results' reliability. For the application of artificial intelligence technology in other educational institutions, it is necessary to obtain student data using a device to measure reaction time and the relative frequency of errors in multitasking tasks and to assess the levels of practical knowledge acquired from instructors. The obtained data will serve to train a neuro-fuzzy model. In addition, the developed technology will allow the discovery of topics and/or simulated practical tasks to improve individual learning for students. For example:  
1. Incorporating practical examples. During the analysis, it was observed that some students consistently performed poorly on tasks that required simultaneous attention to visual and auditory stimuli. This understanding prompts the system to identify \"multisensory task management\" as a problem area for the student. As a result, the system should automatically suggest practical exercises that focus on sensory coordination, such as reaction exercises that involve simultaneous visual and auditory stimuli. 2. Demonstrate feedback mechanisms. For students, the technology should not only identify specific areas of difficulty but also suggest targeted learning exercises. After completing these exercises, the technology should reassess the students' performance, demonstrating a measurable improvement in reaction time and a reduction in error rates. This iterative feedback loop highlights how the technology facilitates continuous improvement and personalized learning. 3. Explain the problem detection process. When processing student response data, the neuro-fuzzy model analyzes trends and deviations in performance indicators such as reaction time and error rate. If certain patterns emerge, such as consistently higher error rates on certain types of tasks, the model assigns a higher degree of membership in the \"problematic\" category for these tasks. This information can then be used to recommend targeted simulated practice tasks, ensuring that the learning process is individualized and focused on those areas where the student has deficiencies. It is noted that this technology can be adapted for training other ATCo students. Since the tasks are focused on assessing the speed, accuracy of reactions, and the ability to multitask, they can be useful for developing the necessary skills in air traffic control.