ENHANCING STUDENT PROFICIENCY IN DATABASE SYSTEMS THROUGH AIBASED ADAPTIVE LEARNING FRAMEWORKS

Abstract

This study explores the enhancement of student proficiency in database systems through an AIbased adaptive learning framework. The purpose of this research is to address the challenges faced in traditional database education, where one-size-fits-all methods often fail to cater to diverse student needs. By leveraging AI, the study aims to create a personalized learning experience that adapts to individual progress and performance. To achieve this, an AI-powered adaptive learning system was designed, incorporating real-time performance assessments and dynamic content adjustment. The experimental setup involved two groups: a control group using traditional methods and an experimental group using the AI-based system. The results demonstrated that the experimental group achieved a 30% improvement in test scores, compared to a 15% improvement in the control group. Additionally, the experimental group showed higher engagement, spending more time on the learning platform. This study’s novelty lies in applying adaptive learning specifically to database systems education, an area that has received limited focus in previous research. The findings highlight the effectiveness of AI in improving student outcomes and engagement in this specialized domain. Future research could explore the long-term effects of adaptive learning in database education and the integration of more advanced AI techniques to further personalize learning paths.