Abstract
Data management plays a significant role in successfully implementing artificial intelligence (AI) in education. The rapid growth of university big data is a problem that needs to be solved in any institution by or for the stakeholders. The study explores the intersection of data management practices and AI applications in education. By synthesizing research, we identify the best practices and common difficulties in automating data management records and data analysis for AI-driven educational initiatives. The study utilized a descriptive-developmental research methodology. Results show that data management and analytics automation significantly reduced manual operations, greatly increased the reliability of existing systems, and effectively reduced data redundancy in records. The study serves as a guide for educators and administrators aiming to optimize data use in their AI initiatives in the academic environment.
Presenters
Raquel AdrianoFaculty, College of Information and Communications Technology, Bulacan State University, Bulacan, Philippines
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Data management, Artificial Intelligence, Analytics, Optimization
