AI-Enabled Pedagogical Framework for Integrating Indian Knowledge Systems in Teacher Education

Abstract

Indian Knowledge Systems (IKS) represent a diverse body of knowledge developed and transmitted through India's intellectual, scientific, philosophical, cultural, linguistic, artistic, and educational traditions. The National Education Policy (NEP) 2020 emphasizes the importance of developing a strong understanding of Indian knowledge, values, traditions, languages, arts, and cultural heritage within contemporary education. However, the integration of IKS into teacher education remains challenging because of fragmented digital resources, heterogeneous knowledge formats, limited personalization, inadequate pedagogical mapping, and the absence of intelligent mechanisms for recommending context-specific IKS learning resources. This paper proposes an AI-Enabled Pedagogical Framework for integrating Indian Knowledge Systems into teacher education. The proposed framework combines Natural Language Processing (NLP), semantic analysis, knowledge representation, recommendation techniques, adaptive learning, intelligent assessment, and learning analytics to support teacher educators and student teachers. The framework consists of five major layers: Data Collection, AI Processing, Pedagogical, Application, and Analytics layers. The Data Collection Layer manages IKS resources obtained from digital repositories, books, manuscripts, educational resources, multimedia content, curriculum documents, and expert-curated materials. The AI Processing Layer performs preprocessing, semantic classification, entity extraction, topic identification, content recommendation, and knowledge graph construction. The Pedagogical Layer maps IKS resources with teacher education learning outcomes and generates adaptive learning pathways. A prototype implementation is proposed through an integrated dashboard containing an IKS content library, AI tutor, adaptive learning module, assessment engine, recommendation system, and learning analytics dashboard. The system can recommend resources related to areas such as Indian mathematics, Ayurveda, Yoga, philosophy, astronomy, agriculture, architecture, linguistics, arts, and traditional pedagogical practices. A controlled evaluation involving teacher education learners can be used to measure recommendation accuracy, learning improvement, user satisfaction, assessment performance, and system usability. The proposed framework provides a technology-supported mechanism for contextualizing Indian Knowledge Systems within teacher education while maintaining human oversight over educational interpretation and knowledge validation. The framework can support personalized learning, curriculum integration, intelligent assessment, and data-driven pedagogical decision-making. Thestudy demonstrates how artificial intelligence can act as an enabling technology for strengthening IKS-based teacher education rather than replacing teacher educators or domain experts.