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Personal characteristics of university students focused on different types of interaction with generative artificial intelligence // Pedagogical Review. 2026. Issue 3 (67). P. 17-28

With the active penetration of generative artificial intelligence into the educational process in higher education the challenge arises of organizing productive student learning activities in this new environment. Since generative artificial intelligence, when applied to learning, can be not only an important resource but also an obstacle to the development of students’ cognitive and creative activity, it is necessary to study student interactions with generative artificial intelligence, which will enable targeted pedagogical regulation of this process. At the same time, it should be noted that insufficient research exists on the types of student interactions with generative artificial intelligence and the personality traits that may influence students’ preferences for one type or another. The aim of this study is to identify the personality characteristics of students oriented toward different types of interaction with generative artificial intelligence. The study is based on a subject-activity approach, according to which the personality characteristics of students as subjects of learning activities can be significant in determining the type of interaction with generative artificial intelligence. The authors substantiated a typology of student interactions with generative artificial intelligence. The empirical study identified the personality characteristics of students oriented toward different types of interaction with generative artificial intelligence. A relationship was established between students’ engagement with artificial intelligence, intrinsic academic motivation, a positive conception of intelligence, and a focus on meaningful learning, as well as their orientation toward “co-authorship” and “leadership” in interactions with generative artificial intelligence. The practical significance of the study lies in the fact that its results can be used to develop recommendations and regulations for organizing university student learning using generative artificial intelligence.

Keywords: higher education, generative artificial intelligence, student interaction types with generative artificial intelligence