Towards Integration of AI Driven Predictive Model in identifying At-Risk Students in Online Learning Platforms

Abstract

The rapid growth of online learning environments presents both opportunities and challenges in student engagement and performance. Predictive modeling using artificial intelligence (AI) has emerged as a promising tool to identify atrisk students and personalize learning experiences. However, the influence ofvarious factors, such as engagement metrics, interventions, and demographicvariables, on student success in online education remains underexplored. This study aims to examine the impact of engagement metrics and targeted interventions, as well as explore the role of demographic variables in predictive modeling within online learning environments. This study employed a simple random sampling method, collecting data from 200 students enrolled in an online learning environment. Data on engagement, interventions, demographic variables, and academic performance were analyzed using descriptive and inferential statistical methods. The findings highlight the importance of engagement metrics in predicting academic success and the effectiveness of personalized interventions such as feedback and tutoring. Integrating personalized interventions can further support at-risk students, creating a more inclusive and effective educational experience. This study underscores the need for continuous development of AI models to better serve diverse student populations and improve educational outcomes.