MODELS AND METHODS FOR SUPPORTING USER-CENTERED INTERFACE DESIGN OF DEFENSE INFORMATION SYSTEMS USING MACHINE LEARNING

Abstract

This paper presents an extended scientific and methodological framework for developing and maintaining user- centered interface design in defense information systems. The proposed approach integrates conceptual modeling, formal representation of interface services, behavioral observation data, and machine learning methods to improve the quality, stability, and predictability of interface performance in mission-critical environments. The relevance of the study stems from the need to ensure consistent usability and operational reliability under rapidly changing mission objectives, diverse user roles, and increased requirements for task accuracy and speed. The paper introduces a multilayer conceptual model that describes the structure of interface services, the parameters of their implementation, their influence on key performance indicators (KPIs), and the role of user behavior characteristics in achieving those indicators. A generalized classification of interface service parameters is proposed, covering structural, functional, perceptual, and manipulative dimensions, as well as contextual conditions of use. Based on accumulated observation archives and cross- system comparisons, the study outlines methods for identifying critical characteristics that significantly affect the quality of interface support. To enhance decision-making across the system lifecycle, several machine learning techniques are proposed: prediction of KPI achievement depending on the selected implementation format; classification of potential interface requirement violations; assessment of the risks associated with insufficient or inconsistent requirements; and evaluation of reengineering or modernization activities. The resulting framework forms a unified data-driven analytical environment that supports design, audit, operational monitoring, and continuous improvement of defense system interfaces based on objective measurements, formal models, and reproducible analytical workflows.