Integrating Indian Knowledge Systems with AI for Personalized Learning to Improve Academic Performance: A Novel Framework

Abstract

Abstract: The increasing adoption of Artificial Intelligence (AI) in education has opened new pathways for personalized learning; however, many existing models overlook cultural and contextual relevance. In the Indian educational landscape, Indian Knowledge Systems (IKS) offer time-tested principles such as experiential learning, self-discipline, and holistic development. This study aims to design an intelligent learning framework that meaningfully combines AI technologies with IKS principles to enhance student learning outcomes.  The primary objective of this research is to develop and assess an AI-enabled system that integrates traditional Indian pedagogical values with modern data-driven approaches. The proposed method employs a hybrid architecture that analyzes student data—including academic performance, behavioral patterns, and engagement levels—using machine learning techniques. Models such as Random Forest and Neural Networks are utilized to generate personalized learning recommendations, while IKS elements are embedded to ensure balance between cognitive, emotional, and ethical development.  To validate the framework, a simulated dataset representing diverse learner profiles is used. The results demonstrate that the integrated model improves student engagement, supports better academic performance, and encourages a more holistic learning experience compared to conventional AI-based systems.  The novelty of this work lies in its interdisciplinary approach, blending traditional Indian educational philosophies with advanced AI methodologies. By aligning with the vision of NEP 2020, this study presents a culturally adaptive and scalable framework that contributes to the evolution of personalized education in India.