Currently, the rapid growth of digitalization and intellectualization of industrial production poses a number of serious problems for professional education, the prompt solution of which determines the efficiency of new science-intensive production complexes. Fast, high-quality training of highly qualified personnel to work with complex industrial automation equipment is one of the most pressing key tasks facing enterprise management and the education system. The acute shortage of a large number of trained specialists in technical specialties in production, as well as the demand for advanced training courses, determine the need to create innovative cognitive technologies for distance learning in engineering specialties on modern industrial equipment in shared-use laboratories. The difficulties that arise in the implementation of this technology are associated with a large number of students, access and remote work on real industrial equipment, as well as management and prompt adjustment of the process of obtaining knowledge and practical skills. These studies are a continuation of a large cycle of works [1,2] on the creation of a distance personalized technology for training engineers based on the approach of the unified artificial immune system (UAIS) for processing multidimensional data on the academic performance of students, predicting results and managing the training process. Current studies propose to improve the efficiency of the UAIS using neuroendocrine algorithms, since in biological organisms there is a close interaction of the neuro-endocrine-immunological systems. With the help of a neural network, informative features of students are identified, on the basis of the endocrine algorithm, operational information on the learning process is processed, and the immune algorithm solves the problem of pattern recognition and prediction of training results. The technology is being tested at the School of Information Technology and Engineering of the Kazakh-British Technical University, accredited by the quality assurance organization Accreditation Board for Engineering and Technology (ABET).

Acknowledgement:
This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic Kazakhstan (Grant No. AP23486386) 2024-2026 г.г.

Keywords: Cognitive technology, distance learning, engineering education, unified artificial immune system with neuroendocrine interaction.