Prospects of using generative neural networks in the training of agricultural engineers

Abstract

The requirements for the quality of theoretical and practical knowledge and skills of agricultural engineers are increasing every year. The use of generative neural networks in the training curricula of relevant specialists satisfies both the prescribed requirements and the requirements for the level of key skills applicable to Industry 4.0 and further on to Industry 5.0. The purpose of the study was to identify the opportunities of using generative neural networks in the training of agricultural engineers. The authors consider the generative neural network as an open resource enabling teachers to design an educational trajectory in accordance with the interests and cognitive goals of the teaching interaction participants. The authors suggest using a system of activities to study the key topics of the “Computer Science and Digital Technologies” course for training major 35.03.06 “Agricultural Engineering”. The paper describes the activities supporting interactive communication, generation of diagrams, graphs and 3D models, searching for original titles, making a list of references, constructing algorithms for solving problems, etc. The authors come to a conclusion that neural networks contribute to improving the quality of training for agricultural engineers due to the following capabilities: presentation of information in various forms, automation of calculations, analysis of large amounts of data, decision support, etc. The digital teaching experience applied to the training of agricultural engineers and the updated teaching content will make it possible to apply neural networks for teaching various subject courses in the future.