Abstract
Modern artificial Intelligence (AI) technologies are increasingly shaping higher education, particularly in their use in training computer science students and integrating neural networks into the learning process. The research aims to evaluate modern approaches and challenges to teaching neural networks to computer science students in higher education. This study employs a quasi-experimental method involving 85 third-year students enrolled in the Computer Science programs at L.N. Gumilyov Eurasian National University and Buketov Karaganda University. The participants were divided into two groups: an experimental group and a control group. The experimental group received instruction with an enhanced curriculum that included modern tools such as TensorFlow, Keras, OpenCV, and Google Colab. Data were collected through pre-tests and post-tests, evaluating changes in student motivation, content comprehension, and technical competencies. Pearson’s chi-square test was utilized to analyze the data, which revealed statistically significant improvements in the experimental group compared to the control group. These results suggest that integrating updated content and hands-on technologies into teaching practices enhances students’ skills and learning outcomes in neural network education. The revised neural network curriculum had a positive impact on student learning outcomes. The research emphasizes the importance of continually updating the curriculum to meet the evolving demands of modern AI.