USING AN ARTIFICIAL INTELLIGENCE-BASED ADAPTIVE DIGITAL LEARNING PLATFORM AND IMPROVING THE METHODOLOGY FOR ASSESSING STUDENTS

Abstract

This thesis examines the use of an artificial intelligence (AI)-based adaptive digital learning platform and its role in improving the methodology of student assessment. The development of digital technologies and AI has created new opportunities for organizing personalized and learner-centered education. Adaptive platforms can analyze students’ learning activities, identify individual strengths and weaknesses, adjust learning materials to their needs, and provide continuous feedback. The study focuses on the pedagogical possibilities of AI-based adaptive learning, particularly in monitoring students’ progress, identifying learning difficulties, and developing individualized assessment strategies. Special attention is paid to formative and summative assessment, automated evaluation, feedback mechanisms, and the use of learning analytics. The thesis argues that the integration of AI into the assessment process can contribute to greater objectivity, efficiency, flexibility, and personalization. At the same time, effective implementation requires attention to data privacy, transparency of algorithms, teacher involvement, and the reliability of automated assessment.