## 5 Discussion  
Using a neuro-fuzzy model, the paper developed an artificial intelligence technology for assessing the level of practical knowledge of ATCo students based on their reactions in multitasking situations. For this purpose, the information model was developed for processing the data of ATCo student reactions in multitasking situations; a neuro-fuzzy model was developed for assessing the level of practical knowledge of ATCo students; the developed artificial intelligence technology was verified on 157,500 accurate data points from ATCo students; and an example of deriving the level of practical knowledge for a selected student S who was tested using a device for measuring reaction time and relative error rate in multitasking tasks is illustrated. Also, the developed technology was tested on student ATcos, and an average error of 7.7% was obtained compared with the ratings received from the instructors. The study logically links the availability of a test database that can be used for the training and analysis of artificial intelligence models with the possibility of assessing the level of practical knowledge of ATCo students. Based on their responses in multitasking situations, AI technology can perform an automated assessment that correlates with the instructors' knowledge levels. This provides an additional level of objectivity and accuracy in evaluation. The artificial intelligence technology is based on data obtained by testing with a device to measure the reaction time and relative error rate in multitasking tasks. The value of the technology is that based on the test data and the assessment of the level of practical knowledge received from the instructor, the training of a neuro-fuzzy network is carried out to create a research knowledge base; data processing is carried out using the theory of fuzzy sets and intellectual analysis of knowledge; and an evaluation of the level of practical knowledge of the evaluated students is carried out using the knowledge base of the study. The mathematical apparatus of the theory of fuzzy sets, intellectual analysis of knowledge, and neural networks are used to formalize the data. Data processing from the testing device is based on the theory of fuzzy sets and membership functions, which are components of intellectual knowledge analysis. Based on their reactions in multitasking situations, a neuro-fuzzy model was used to determine students' practical knowledge level, which was verified on accurate data. Such a mathematical toolkit increases the degree of validity of decision making and can reveal complex and non-linear dependencies in the data. The presented research is based on models driven by data and the knowledge of training instructors and has significant practical value. The advantages of the developed technology are that the information model is based on accurate data obtained from the device and the expert assessment of the training instructor. The neuro-fuzzy model can change the setting of synaptic weights relative to the importance of a given task and the repetition of the exercise, and by training the network, the knowledge base can be supplemented. A limitation of our study was the sample of ATCo students from our Technical University of Kosice (Slovakia) for training the neuro-fuzzy model. To compare the results of the experiment, we need, in the future, to obtain data from other educational institutions as well. To overcome these limitations, future studies will involve more educational institutions, providing a greater diversity of data, which will increase the generalizability of the results.