Problems and Prospects for the Application of Neural Networks for the Sphere of Education
Authors: Shamsutdinova T. M. 
Published: December 2022

The purpose of the studyis to examine the theory and practice ofusing neural networks in education, to develop the concept of aneural network adaptive learning environment, and to implementa neural network model of one of the subsystems of this environment (using a model example of creating an adaptive educationaltrajectory).Materials and methods.The study includes a review of bibliographicsources on the application of neural networks in the field of education.It also includes modeling the structure of a neural network adaptivelearning environment. The PyTorch library was used to programmatically implement the neural network.Results.The prospects for the use of neural networks in the field ofeducation are considered, including various tasks of recognition,diagnostics, classification, clustering, forecasting, optimization,etc. A structural model of an adaptive learning NeuroSmartenvironment is created. This environment includes a number ofsubsystems for solving the following tasks: biometric identificationof the student; determining starting level; choosing an adaptivelearning path; determining the current level of competency development; automated verification of students’ work using recognitiontechnology; analysis of the final result of training; monitoringinformation security incidents in an electronic course, etc. Inorder to study the possibilities and problems of applying neuralnetwork models to the tasks of constructing student adaptivelearning trajectories, a model example of a neural network wascreated. This example illustrates the possibility of using a neural network to select further nodes of the educational trajectorybased on the available data on the current learning parametersin an electronic course. To implement the neural network, thePyTorch deep learning library and Pandas library modules wereused. SGD, Adam, Rprop were used as an optimizer to performgradient descent steps. For each of the optimizers, a computerstudy of the stability of the neural network was carried out byvarying the following parameters: the learning rate coefficient,the number of neurons in the hidden layer, and the number oftraining epochs.Conclusion.It can be assumed that the next stage in the evolutionof the use of neural network technologies in the field of educationwill be their integration into complex multi-component Smart systemscapable of automatically supporting student learning at all stagesof the implementation of their personal educational trajectory. Obviously, the practical implementation of complex neural networksystems of this level is a very difficult task and can be solved so faronly at the level of individual subsystems. There are a number of problems associated with computer simulation of the educationalenvironment based on neural network models: the question of theoptimal structure of the neural network (for example, the numberof neurons and layers in the network) has not been studied enough,there are no clear criteria for the optimality of the adaptive educational trajectory. Nevertheless, it should be noted that the task ofdeveloping new forms and technologies of personalized e-learning isin great demand, which makes modeling based on neural networksespecially relevant.