The subject of the research is the methodological basis for studying users' digital competence and trust in artificial intelligence in the educational environment. The article examines theoretical approaches to understanding digital competence, trust, trust in technologies, digital trust, and trust in artificial intelligence. Digital competence is understood as a multi-component characteristic of a person, encompassing knowledge, skills, critical thinking, motivation, responsibility, and the ability to act meaningfully in a digital environment. Trust in artificial intelligence is viewed as a socio-psychological construct related to perceptions of reliability, usefulness, predictability, transparency, and the acceptability of using intelligent systems. Special attention is paid to the educational environment and the student audience as a space where new forms of interaction between humans and intelligent technologies are most vividly manifested. The methodological foundation of the article consists of a theoretical analysis of the scientific literature, comparative and analytical analysis, as well as an analysis of regulatory and analytical materials devoted to digital competence, artificial intelligence, and trust in intelligent systems. The main conclusions of the conducted research include the justification for the need to jointly consider digital competence and trust in artificial intelligence within a unified research framework. It is shown that in foreign studies, trust in artificial intelligence is predominantly described through parameters of reliability, predictability, transparency, effectiveness, and explainability of algorithmic solutions. In domestic psychology, this construct is more often related to a broader context of digital behavior, the experience of security, criticality, responsibility, and maintaining a subjective position when interacting with technology. The novelty of the research lies in the clarification of the methodological foundations for studying trust in AI in the educational environment and in justifying its connection to users' digital competence. It is concluded that this approach allows for a deeper description of the characteristics of students' digital behavior and for selecting diagnostic tools for subsequent empirical research.
Yahudina et al. (Sun,) studied this question.