An algorithm for creating adaptive exercise scenarios on a simulator using dynamic complexity to improve the effectiveness of ATCO training

This work is devoted to the development of an innovative algorithm for creating adaptive simulator scenarios for training air traffic control (ATC) officers using dynamic complexity. The relevance of the study is caused by the rapid increase in air traffic intensity, which requires fundamentally new approaches to training of specialists. Traditional simulator training methods based on the manual creation of a scenario by an instructor do not take into account the individual characteristics of students, which reduces effectiveness of the learning process and can lead to cognitive overload. The main goal of this research is to create an intelligent system capable of automatically adapting the complexity of exercises in real time, taking into account the current skill level, decision-making speed, error rate and the psychophysiological state of the controller. The paper offers an integrated approach combining the analysis of professional competencies, modeling cognitive load generation of training situations. Special attention is paid to the balance between the gradual complication of tasks and the prevention of stress overload. The research methodology includes the development of a mathematical model for assessing the student’s level, an algorithm for dynamically adjusting scenario parameters (e.g., number of aircraft, weather conditions, emergency situations) and a feedback system. The developed system allows you to create personalized training programs that are as close as possible to real working conditions, but with a controlled level of complexity. The practical significance of this work lies in the possibility of implementing the proposed solutions into existing training complexes, which will contribute to improving the quality of ATC training and as a result, air traffic safety. The scientific novelty is confirmed by the author’s developments in the field of students adaptive learning and the integration of biometric indicators into exercise generation process. The prospects of further research are related to the expansion of the base of training scenario database, the introduction of virtual reality technologies and the development of intelligent systems for analyzing learners’ actions based on machine learning methods. The proposed approach can also be adapted for other high-responsibility professions requiring quick decision-making in stressful conditions.

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