Abstract
a bioinspired approach based on a unified artificial immune system featuring neuroendocrine regulation of homeostasis during knowledge acquisition. To implement an intelligent distance learning system for the control of complex industrial automation objects – based on the proposed UAIS–NES technology – an integrated ontological model has been developed. This model consists of ontological sub-models for the immune, neural, and endocrine subsystems, serving as the foundation for a knowledge base designed to integrate cognitive technology into the educational process. Classes and subclasses have been defined, and their interactions analyzed, to facilitate a two-stage data processing workflow: specifically, the reduction of non-informative features and subsequent classification, both executed using bio-inspired algorithms. System quality assessment criteria have been established based on metrics categorized into mutually exclusive classes, distinguishing between favorable and unfavorable outcomes. Based on the results of data analysis utilizing this technology, a forecast is generated regarding the level of acquired professional engineering skills, the probability of successfully mastering the academic discipline, and the student’s degree of readiness to tackle complex technical management challenges – all presented in the form of a digital student competency profile.