AI and Data Science in Inclusive Education: Building Predictive Models to Enhance Diversity Support Systems

Abstract

Inclusive education strives to provide equitable learning opportunities for all students, regardless of their diverse backgrounds and needs. However, identifying and addressing the unique challenges faced by diverse student populations remains a complex task for educational institutions. This research explores the integration of Artificial Intelligence (AI) and Data Science in developing predictive models aimed at enhancing diversity support systems within inclusive education frameworks. By analyzing comprehensive datasets encompassing student demographics, academic performance, engagement metrics, and support service utilization, we employ machine learning algorithms to predict students' support needs and potential barriers to success. The study evaluates the effectiveness of various predictive models, including decision trees, random forests, and neural networks, in accurately identifying at-risk students and tailoring support interventions. Additionally, we address ethical considerations related to data privacy, bias mitigation, and the interpretability of AI-driven decisions. Our findings demonstrate that AI and Data Science significantly improve the precision and responsiveness of diversity support systems, thereby fostering a more inclusive and supportive educational environment. This research provides actionable insights and a framework for educational institutions to leverage AI technologies in promoting diversity and inclusion.