## 4 Results  
The research verified and tested the artificial intelligence technology for assessing the level of practical knowledge of ATCo students based on their reactions in multitasking situations on 157,500 points of real data of the Technical University of Kosice (Slovakia) in the \"PILOT\" study program at the Faculty of Aeronautics [29]. Data collection was carried out systematically during the last academic year (2023/2024) of the practical training of air traffic control students. All students underwent a rigorous selection process consisting of a medical examination, a psychological examination, and a language proficiency test. Due to the strict entry requirements, the data collection took a long time. Based on the data obtained from 35 students, many experiments were conducted to configure the artificial intelligence technology and train a neuro-fuzzy model to assess the level of practical knowledge of the students to obtain synaptic weights \(\alpha_{1},\alpha_{2},\ldots,\alpha_{7}\). An example of technology verification was carried out on the following three ATCo students: n36, n37, n38. The group of students studied consisted mainly of bachelors aged around 19-21. The age homogeneity of the sample allowed us to evaluate the results of the study within a typical cohort group for this stage of education. The students underwent training in theoretical subjects intended for training air traffic controllers as well as practical classes on the Aviation Visual Information System simulation software (The ICZ LETVIS(r) SIM SW, OS LINUX SLED/SLES 12 AND HIGHER), which is used to practice radar control procedures and situations that arise in air traffic control work. The experimental process in the study included several stages:  
1. Data collection--The data collection process was systematic and took place during the practical training of students in aviation air traffic control. The students underwent a careful selection process that included a medical examination, psychological testing, and language proficiency testing, which ensured a high level of sample homogeneity. 2. Tuning the AI technology--Based on the collected data, numerous experiments were conducted to tune the AI technology and train the neuro-fuzzy model. The goal was to determine the synaptic weights (\(\alpha_{1}\), \(\alpha_{2}\)..., \(\alpha_{7}\)) used to assess the level of practical knowledge of the students. This stage required significant data processing and tuning of the model parameters to achieve high accuracy of assessments. 3. Technology verification--As part of the technology verification, an experiment was conducted on data from three ATCo students (n36, n37, n38). This allowed us to test how the AI system assessed the level of practical knowledge in real multitasking conditions and how effectively the model coped with analyzing the students' reactions. In general, the experiment included the stages of data collection, tuning the artificial intelligence model, and testing it on real-world examples to confirm its effectiveness in assessing the practical knowledge of the ATCo students. Based on the entire set of data obtained, several experiments were conducted to set up the artificial intelligence technology and train a neuro-fuzzy model for assessing the level of practical knowledge of ATCo students to obtain the synaptic weights \(\alpha_{1},\alpha_{2},\dots,\alpha_{7}\). To illustrate the step-by-step evaluation process, an example of the performance level derivation for a selected student, ATCo S, tested using a device measuring reaction time and relative error rate in multitasking tasks is illustrated. The developed artificial intelligence technology consists of an information model of data processing (\(I_{FI}\)) and a neuro-fuzzy model of assessing the level of practical knowledge (\(M_{NT}\)). In the first stage, testing takes place using a data acquisition device, and the data are processed using the information model \(I_{FI}\).