AI-POWERED ADAPTIVE LEARNING SYSTEMS: PERSONALIZING EDUCATION THROUGH REAL-TIME DATA ANALYTICS AND STUDENT BEHAVIOR MODELING

Abstract

This study explores the effectiveness of AI-powered adaptive learning systems in enhancing student performance, engagement, and the overall learning experience. Through an experimental approach, the research evaluates the academic performance, engagement levels, and perceptions of both students and teachers in control and adaptive learning groups. The post-test score data showed an 18% improvement by students in the adaptive group while the control group only improved by 3% which indicates the adaptive learning system brought major academic benefits.  Students in adaptive learning classes spent 45 minutes per session while engaging with system content four times per week which was greater than the control group students who dedicated 30 minutes per session with two sessions per week.  The instructional staff from the adaptive learning group rated the system higher than those from the control group while system comments mentioned both its effectiveness and usefulness.  Student participation increased primarily through the combination of personalized content feedback and relevant learning materials according to research findings.  The cost-benefit evaluation demonstrated that performance enhancements of the adaptive learning system proved its value despite expensive initial installation costs and ongoing maintenance expenses.  Empowered by artificial intelligence adaptive learning demonstrates potential to change educational practices through customized learning approaches which lead to improved academic outcomes combined with student engagement and generate valuable insights to support system advancement and scaling across diverse educational settings.