Artificial Intelligence-Driven Predictive Analytics for Educational Behavior Assessment

Sri Bhargav krishna Adusumilli, Harini Damancharla, Arun Raj Metta

Abstract


Artificial Intelligence (AI)-driven predictive analytics is revolutionizing the assessment of educational behavior by enabling more accurate, data-driven insights into student performance and learning patterns. This paper explores the application of AI in analyzing educational behavior, focusing on the use of machine learning algorithms to predict academic outcomes, identify learning difficulties, and provide personalized recommendations for students. By leveraging large datasets of student interactions, assessments, and behavioral patterns, AI models can uncover hidden trends and offer predictive insights that inform teaching strategies and interventions. This approach not only enhances the understanding of individual learning behaviors but also supports the creation of adaptive learning environments tailored to diverse student needs. The study discusses the methodologies employed in developing AI-based predictive models, the challenges in data collection and privacy, and the potential impact on educational practices. The findings suggest that AI-driven predictive analytics can significantly improve educational assessments, helping educators make informed decisions to foster student success.

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