AI-Driven Personalised Learning Models Inspired by Indian Knowledge Systems in Modern Classrooms

Authors: Mrs. Madhu Priya

Country: India

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Abstract: The rapid expansion of Artificial Intelligence (AI) in education has opened new possibilities for personalised learning, yet existing models often lack cultural contextualisation and holistic pedagogical grounding. This study explores the integration of AI-driven personalised learning with Indian Knowledge Systems (IKS) to create learner-centric, culturally rooted, and adaptive educational environments for modern classrooms. A significant research gap exists in connecting ancient Indian pedagogical traditions—such as the Gurukul model, Swadhyaya (self-directed learning), Tarka (logical inquiry), and Anukarana (learning through observation)—with contemporary AI methodologies. To address this, the study employs a secondary data methodology, drawing insights from scholarly literature, NEP 2020 policy documents, IKS frameworks, and digital research repositories. The analysis indicates that AI personalisation strategies naturally align with IKS practices: mentor-based adaptive feedback reflects the guru–shishya system, self-paced modules mirror Swadhyaya, reasoning-centred AI tutoring parallels Tarka, and multimodal learning technologies replicate Anukarana. These findings suggest that integrating IKS principles into AI-powered platforms can foster holistic learning, enhance cognitive engagement, and support value-based education. The implications extend to curriculum designers, policymakers, EdTech developers, and educators aiming to implement culturally meaningful AI systems. Overall, this study presents a conceptual foundation for building future-ready AI learning models inspired by India’s rich educational heritage.

Keywords: Artificial Intelligence in Education (AIED), Indian Knowledge Systems (IKS), Personalised Learning Models, Gurukul and Swadhyaya Pedagogy, AI-Driven Adaptive Learning

Paper Id: 232857

Published On: 2025-02-10

Published In: Volume 13, Issue 1, January-February 2025

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