Development of Students’ Self-Learning Competence in Teaching Political Theory Subjects in the Digital Environment

Abstract

The development of digital technology and generative artificial intelligence has changed the conditions under which university students access, process, and use knowledge, while also reshaping the organization of self-learning activities. In Political Theory subjects, these changes are associated with the need to understand concepts accurately, identify relationships among theoretical propositions, select and verify learning materials, develop arguments, and apply theoretical knowledge. This paper adopts a theoretical approach, using methods of analysis, synthesis, systematization, and generalization of relevant literature. Students’ self-learning competence is examined from three dimensions: self-direction and organization of learning; access to, selection, verification, and processing of learning materials; and application of knowledge, self-assessment, and adjustment. The paper analyzes changes in self-learning activities with the increasing use of generative artificial intelligence in learning and discusses orientations for teaching organization, assessment, and the conditions required to develop this competence. Digital technology and artificial intelligence are viewed as tools that support cognitive activities; identifying problems, verifying information, constructing arguments, and taking responsibility for learning products remain the responsibility of learners.