Artificial Intelligence Technology for Assessing the Practical Knowledge of Air Traffic Controller Students Based on Their Responses in Multitasking Situations

The main goal of the research is to develop an artificial intelligence technology to assess the practical knowledge of air traffic controller (ATCo) students based on their responses in simulated multitasking situations using the proposed neuro-fuzzy model verified in experiments. An informational neuro-fuzzy model was developed and verified on 157,500 real data points. It illustrates an example of inferring the level of practical knowledge in selected ATCo students who were tested using a device measuring the reaction time and relative error rate in multiple-task tasks. The average error in the incorrect response was 7.7% of the experimental data. Data processing was performed using fuzzy set theory and intellectual knowledge analysis. These measurement results are useful for an individual approach to the student’s education to understand and master the correct solutions to achieve the desired educational results. Ensuring a personal approach to the student’s education is key to acquiring the necessary skills, knowledge, and competencies in the profile of the graduate. The developed technology will enable the integration of automated knowledge and skills assessment systems into the real educational process and the identification of problematic topics and tasks in the training of individuals. The result of the conducted research was used for the software design for the practical application in the flight training of ATCo students.