MONITORING AND LEARNING ANALYTICS MECHANISMS IN AN AI-BASED INTERACTIVE TEACHING SYSTEM FOR DEVELOPING STUDENTS’ PROFESSIONAL COMPETENCE (Eureka Journal of Education & Learning Technologies, 2026)
The rapid development of digital learning environments requires the implementation of innovative approaches that ensure continuous monitoring, personalization, and effectiveness of the educational process. This article presents the development of monitoring and learning analytics mechanisms within the SQLUSTOZ.UZ AI Mentor platform designed to improve students’ professional competencies in teaching the “Database” course. The proposed approach integrates Learning Analytics (LA), Educational Data Mining (EDM), adaptive recommendations, and formative assessment mechanisms. The platform collects and analyzes students’ learning activity indicators, including system usage frequency, task completion time, number of attempts, SQL query errors, usage of optimization tools, and assessment results. Based on analytical data, individual competency profiles and personalized learning trajectories are generated. The developed mechanism provides opportunities for identifying knowledge gaps, adapting educational content, and supporting evidence-based pedagogical decision-making.
Keywords: Learning Analytics, Educational Data Mining, Artificial Intelligence, adaptive learning, professional competence, interactive teaching system, database education.