## 5 Discussion
(**a**) The point of gaze focus matches the center point of the object; (**b**) the point of gaze focus is inside the object area but does not match the center point of the object and the area of error is within the boundary of the object area; (**c**) the point of gaze focus is inside the object area but does not match the center point of the object and the area of error is partially within the boundary of the object area; (**d**) the point of gaze focus is outside the object area and the area of error is partially within the boundary of the object area; (**e**) the point of gaze focus is outside the object area and the area of error is outside the boundary of the object area. Figure 9: Various scenarios of eye-tracking technology application for the real example. (**a**) The point of gaze focus matches the center point of the object; (**b**) the point of gaze focus matches the center point of the object; (**c**) the point of gaze focus matches the center point of the object and simplification of the object’s shape was applied; (**c**) the point of gaze focus does not match the center point of the object and simplification of the object’s shape was applied. natural head movements, which can significantly impact assessment precision. Thus, it is recommended that future studies focus on enhancing eye-tracking technology's accuracy, particularly in real-world operator conditions. Optimization of eye-tracking systems should involve technological improvements and analysis of various environmental factors affecting operators' working conditions. Testing these systems under different configurations, such as multiple screen types and lighting conditions, will provide better insight into how these factors influence measurement accuracy and system performance. Simultaneously, adaptive eye-tracking models that account for individual operator characteristics, such as age or visual impairments, need to be developed. These systems could dynamically adjust to specific user needs, increasing their practicality and reliability. Another critical aspect of future research is the development of more advanced detection algorithms. Current technologies cannot precisely recognize the various shapes and sizes of prohibited items, which impacts operators' effectiveness in identifying potential threats. Developing more sophisticated algorithms that consider these variables could significantly enhance the accuracy of detecting dangerous objects. Furthermore, integrating eye-tracking systems with artificial intelligence tools could represent the next step in advancing operator evaluation technology. AI-based systems could automatically classify and analyze eye-tracking data, identifying patterns and potential anomalies in operator behavior. Such integration could lead to more efficient and autonomous evaluation systems that support operators' training and certification processes. The proposed research directions aim to improve the accuracy of evaluation systems and enhance their practicality and adaptability to real operational conditions. Implementing these solutions could significantly improve the effectiveness of security screening operators, thereby contributing to a higher overall level of safety at airports.