LEARNING ANALYTICS WITH AI: MONITORING PROGRESS AND PEDAGOGICAL INTERVENTIONS

Abstract

This study investigated the use of artificial intelligence–supported Learning Analytics to monitor student progress and guide pedagogical interventions. The problem addressed how this approach contributed to tracking learning trajectories and supporting teaching decisions while considering ethical implications, inclusion challenges, and the need for data governance in educational contexts. The general objective was to analyze, based on the literature, how AI-enabled Learning Analytics supported progress monitoring and the implementation of pedagogical interventions, discussing pedagogical, ethical, and inclusive implications. A bibliographic research methodology was adopted, using an interpretive analysis of selected academic works on AI in education, authorship, ethics, and governance-related sectoral applications. In the development section, the study systematized foundations of intelligent monitoring, data sources, and interpretive limits of indicators, as well as evidence-informed preventive, formative, and corrective intervention typologies, emphasizing the centrality of human mediation. In the final considerations, it was concluded that AI-supported Learning Analytics increased the visibility of learning patterns and enabled more timely interventions; however, its effectiveness depended on contextual pedagogical interpretation, ethical safeguards, inclusion commitments, and governance structures, indicating the need for empirical studies to assess impacts and mitigate bias.