Perceptions of Neural Network Use in Higher Education: Case Study

Abstract

The integration of artificial neural networks (ANNs) into various fields, particularly education, has recently garnered considerable attention because of their potential to improve learning processes and optimize administrative tasks. This article aims to explore the potential of neural networks in the context of higher education, based on a case study conducted in Southern Federal University. The study employed a mixed methodological approach combining quantitative surveys, pedagogical experiments, and qualitative interviews. The study involved 132 3rd and 4th-year university students divided into a control group (CG) and an experimental group (EG). EG students were subjected to educational processes involving ChatGPT and other ANN-based tools, while CG students adhered to traditional teaching methods. The obtained data were analyzed using mathematical statistics, including Pearson's χ2 test, to compare the digital skills and perceptions of the two groups. According to the results, EG students significantly improved their digital skills compared to CG students. Students generally had a positive opinion about ANNs, recognizing their ability to facilitate learning and save time. However, concerns about the reliability and potential biases of the information provided by ANNs were also noted. The study concludes that ANNs have significant potential to improve the quality of higher education by enhancing learning efficiency and reducing administrative burden. Recommendations for the implementation of ANNs in higher education are provided. The findings show that neural networks in higher education have great potential to improve the learning process.