Algorithm of neural networks integration into the work of high school instructors to enhance the quality of the educational process

The objective of this article is to develop an algorithm for integrating neural networks into the digital environment of higher education institutions to optimize the work of educators and enhance the quality of the educational process. The options for optimizing the teachers’ work are considered: 1) automated creation of curriculum and working programs; 2) generation of educational materials; 3) adaptation of educational materials to the needs of students; 4) written assignments; 5) monitoring the level of students understanding of the material and providing recommendations for improving student performance; 6) instant feedback from students; 7) completion of reporting documents. The criteria for improving the quality of education are described: flexibility and adaptability of the educational process to the needs of the labour market through the use of modern and high-tech digital tools in the learning process; students skill to work in a digital environment, which will be used by graduates as a professional advantage in the future; a creative approach by teachers to the learning process, making it more interesting, engaging, and motivating. The article analyzes the characteristics of neural networks in order to identify their functions, describe them, and utilize them in the work of higher education instructors for automating routine tasks and reducing the time spent on standard activities that do not require direct involvement from the educator. The scientific novelty lies in the description of the developed algorithm for integrating neural networks into the digital environment of higher education, where neural networks, demonstrating high technical potential, improve the work of university educators. The algorithm consists of nine main steps: 1. Defining the tasks for integrating neural networks into the university's digital environment to optimize and automate the methodological work of teachers; 2. Creating a regulatory framework to govern the rights and opportunities of students, teachers, and university staff; 3. Selecting a technically suitable neural network depending on the defined tasks; 4. Training the neural network; 5. Technical work on creating a user-friendly interface for the neural network; 6. Implementing neural networks directly into the teacher's work; 7. Monitoring the effectiveness of the neural network implementation; 8. Gathering feedback from students and staff as a result of the successful implementation of neural networks; 9. Developing and adding new features and capabilities to the neural network. The results of the study are as follows: 1) the functionalities of neural networks aimed at optimizing and providing methodological support for the work of higher education instructors have been identified and described; 2) the functionalities of neural networks for solving administrative tasks within the university have been identified and described; 3) based on the identified functionalities of neural networks, a step-by-step algorithm for their integration into the digital environment of the university has been outlined.