AI AND DATA SCIENCE IN EDUCATION: BENEFITS, CHALLENGES, AND THE PATH FORWARD

Abstract

Artificial Intelligence (AI) and data science are transforming the educational landscape, offering powerful tools that enhance learning experiences, operational efficiency, and accessibility. This article explores the multifaceted role of AI-driven systems—such as adaptive learning platforms, intelligent tutoring, and automated grading—and the ways data science enables personalized, data-informed learning environments. These technologies allow for the customization of educational content to match individual learning styles and needs, promote early intervention for at-risk students, and streamline resource management in educational institutions. While the potential benefits are significant, the integration of AI and data science in education presents distinct challenges. Key concerns include privacy and data security, the risk of bias in AI algorithms, reduced human interaction impacting social-emotional learning, and the digital divide that limits access for some students. Addressing these issues requires thoughtful, ethical approaches to data governance, bias mitigation, and ensuring equitable access to technology. The article provides a path forward for implementing AI and data science responsibly in education. Recommendations include establishing transparent data policies, designing fair algorithms, and balancing technological tools with human interaction to create an inclusive and adaptive educational environment. This approach promises a future where AI and data science support more effective, personalized, and accessible learning experiences for all students.

Keywords: Artificial Intelligence, data science, education, adaptive learning, intelligent tutoring, automated grading, data privacy, data security,