Artificial Intelligence in Remote Education and Its Effect on Academic Performance of Students

The rapid expansion of remote education has fundamentally transformed contemporary learning systems, generating extensive digital learning data and redefining instructional practices. This study investigates the impact of AI-enabled remote education on students’ academic performance using a machine learning–oriented analytical framework. Key factors examined include technological accessibility, AI-based learning support, student engagement, instructor–student interaction, self-regulated learning skills, satisfaction levels, and academic outcomes. Primary data were collected from 60 undergraduate students through a structured questionnaire. Descriptive and analytical techniques were applied, including percentage analysis, the Elbow Method to determine the optimal number of clusters, and K-Means clustering to classify students based on learning behavior, performance patterns, and satisfaction levels. The findings reveal that students with stable internet access and AI-supported learning platforms demonstrate improved academic performance and higher engagement levels. Conversely, technological barriers and limited interaction negatively affect learning outcomes. The study provides data-driven insights that can assist educators and institutions in optimizing AI-integrated remote education strategies to enhance academic achievement and learner satisfaction.