INTEGRATING MULTIMODAL AI AND PREDICTIVE ANALYTICS FOR REAL-TIME CHILD BEHAVIOR MONITORING IN EARLY EDUCATION SETTINGS

<p>The early <a href="https://ijetrm.com/issues/files/Aug-2025-20-1755707731-AUG25.pdf" target="_blank" rel="noopener">education monitoring </a>of child behavior in early education is important to assist the development of the<br>child, conflict handling and to improve learning in the child. The established observational approaches are usually<br>subjective, constrained by time, and scope, which spawns the necessity of new technological applications. The<br>present paper discusses multimodal artificial intelligence (AI) and predictive analytics to be implemented to observe<br>child behavior in real-time during early education. Multimodal AI enables contactless monitoring of children,<br>including their behavior in the classroom based on a combination of data on their behavior across modalities,<br>including facial expressions, speech patterns, body movements and classroom interactions. Predictive analytics<br>further empowers this system through the aspect of predicting the possible trends in behavior and the early warning<br>signs of learning challenge or emotional discomfort. The suggested model illustrates how the approach based on AIdriven monitoring would provide teachers and caregivers with practical information to enhance subjectivity in<br>behavioral assessments, making interventions timely. Along with it, the paper speaks about ethical and privacy<br>issues of implementing AI in sensitive educational contexts and methods of deploying it securely, bias-consciously,<br>and transparently. The evidence indicates that incorporating predictive analytics along with multimodal AI would<br>revolutionize early education because of its potential to encourage personalization learning in the classroom, help<br>manage students more effectively, and enhance the long-term child development.</p>