## 2 Overview of Selected Domestic and Foreign Research Studies  
In recent years, we have observed the development of automation and artificial intelligence in control processes in civil aviation [1]. Within the air traffic management process, it is essential to monitor the ability of team members to anticipate and identify errors and adapt to unforeseen events [2]. With the increasingly severe problem of airspace congestion, the aviation industry faces enormous challenges, including a sharp increase in pressure on air traffic controllers, a significant increase in flight delays, and frequent conflicts [3]. Effective conflict detection and resolution technology is paramount to ensure flight safety, which is crucial for the complex, high-density airspace environment [4]. Research in this area is also relevant to preventing aircraft collisions, relieving pressure on air traffic controllers, and predicting the cognitive load on air traffic controllers [5]. People working in modern work systems, especially air traffic control, are increasingly required to oversee the automation of tasks [6]. Enhancing the generalizability and reliability of simulation models in air traffic control is challenging [7]. When predicting the workload of air traffic controllers, the tracking of reaction time in multitasking tasks can be considered and is the main monitored parameter [8]. Various methods can be used to measure reaction time. One of the methods is eye-tracking using a video camera, where the result is a comparison of the time difference in response to visual, acoustic, and audiovisual stimuli [9]. Previous research has found that air traffic controllers' reactions to visual stimuli are more accurate but slower than their corresponding performance in reaction to auditory stimuli [10]. In our previous research, a device was developed to measure the reaction time of air traffic controllers that monitored their reaction speed in the handling of and response to visual and auditory cues. We evaluated the results based on fuzzy modeling, neural networks, and AI involvement [11]. Air traffic controllersare on duty 24 h daily based on multiple shifts [12]. They work between day and night and have different schedules for day and night shifts, which can affect biorhythms and sleep quality [13]. Increased fatigue decreases the ability to process information [14]. The problem of air traffic control automation is a significant problem that is constantly being investigated because it offers a concrete solution in the face of the exponential growth of air traffic, an increase that is estimated for the next 30 years to be an urgent problem that requires an early resolution [15]. Our research [16] demonstrates the use of AI in qualitative and quantitative research data analysis using neuro-fuzzy modeling. The research focused on optimizing personalized training methods, assessment, and feedback systems to improve the comprehensiveness and accuracy of aviation specialist training evaluations [17]. This study examines how advanced data analytics and artificial intelligence (AI) can work together to improve air traffic controllers' decision-making processes [18]. As we navigate today's data-driven environment, discovering synergies between these fields is critical, given the increasing complexity of data sets. Personalized learning is based on individual interests, learning styles, and self-pacing. It provides focused and self-directed learning. The education system is advanced in various aspects. Hence, personalized learning with the help of AI provides adaptive learning [19]. A customized approach to the learning process using AI and fuzzy modeling helps learners succeed in training based on feedback analysis and the prediction of their progress [20]. AI brings unique capabilities to address complex challenges in training aviation personnel, emphasizing increased efficiency, task automation, enhanced quality and consistency of measured data, advanced analytics, and performance report generation [21].