ARTIFICIAL INTELLIGENCE APPLICATIONS IN SOCIAL PEDAGOGY: PREDICTIVE ANALYTICS FOR EARLY INTERVENTION ENHANCEMENT (EDUCATION IN THE 21ST CENTURY, 2025)
Early identification of children and families in need of support is a critical task in social pedagogy. This paper examines how Artificial Intelligence (AI) systems can augment the social pedagogue’s work by predicting risk factors and detecting the need for early interventions. We present a study using a predictive analytics approach to flag at-risk students, applying a regression model to educational and socio-demographic data. The model’s results indicate that AI-driven analytics can successfully identify a significant portion of at-risk youths, allowing interventions before issues escalate. We discuss these findings in the context of existing literature, highlighting the benefits of AI—improved accuracy, efficiency, and resource allocation—alongside the challenges, such as ethical considerations and the need for human oversight. The study concludes that AI systems, when used responsibly, have the potential to greatly enhance early intervention strategies in social pedagogy, supporting social pedagogues in making informed, timely decisions to improve outcomes for vulnerable populations.
Keywords: Artificial Intelligence (AI); Predictive Analytics; School Dropout Risk; Early Intervention; Social Pedagogy; Machine Learning Models; Bias and Fairness in AI; Data Privacy; Decision Curve Analysis (DCA); Educational Data Mining