MONITORING STUDENTS' LEARNING OUTCOMES AND ASSESSING INDIVIDUAL DEVELOPMENT WITH ARTIFICIAL INTELLIGENCE IN A DIGITAL EDUCATIONAL ENVIRONMENT

This paper provides a scientific and methodological analysis of monitoring students' learning outcomes and assessing their individual development with artificial intelligence (AI) in a digital educational environment. It examines the content of learning outcome monitoring, the components on which it relies (data sources, indicators, analytical methods and feedback), and the opportunities offered by AI-based learning analytics. It also defines three groups of criteria for assessing individual development: the dynamics of results, the achievement of learning goals, and independent work and reflection. As a result, a six-stage methodology is proposed in which the teacher keeps the final pedagogical decision. The methodology is intended to support the timely and individualised assessment of students in higher education institutions.