Application of Generative Artificial Intelligence in Education Personalization: Perceptions of Humanities and Technical Students

Introduction. The intensive integration of generative artificial intelligence technologies has led to significant transformations across various fields, including education. The expansive development of information technologies has enabled the simplification of education personalization, enhancing its quality, accessibility, and methodological diversity. The advantages of these new technologies compel the scientific community to seek effective methods and approaches for applying artificial intelligence in learning and to anticipate upcoming changes in the educational environment. The aim of this study is to examine the experience of humanities and technical students using chatbots and to analyze students’ perceptions of educational process personalization through artificial intelligence. Materials and Methods. The empirical basis comprises data from a sociological survey conducted from June 2024 to January 2025, involving 730 undergraduate and graduate students from Russian universities. The survey was administered via online questionnaires designed to test the formulated scientific hypotheses. Results. The findings demonstrate a positive attitude among students towards the systematic implementation of artificial intelligence in the educational process and the utilization of personalized learning elements (digital mentors and chatbots) that foster improved interaction and support during learning. Negative sentiments were identified among students regarding the use of artificial intelligence for assessing their academic performance, indicating insufficient trust in the objectivity and accuracy of such technologies in evaluation. The necessity for further analysis and the development of strategies for the effective integration of artificial intelligence into the educational environment, taking into account student needs and concerns, is emphasized. Notably, the perceptions of humanities and technical students differ only slightly. Discussion and Conclusion. The theoretical and methodological provisions developed in this article complement existing approaches and tools for studying the impact of modern technologies on the educational process. The conclusions drawn by the authors can be utilized by university administrators and government bodies for developing strategies for artificial intelligence application in education. Additionally, the results may be of value to curriculum designers and methodologists seeking to employ artificial intelligence in the development of learner-centered courses, where students are central to the educational process and their opinions are key in creating individualized learning paths.