AI-Driven Adaptive Learning Systems and Gamified Platforms: Advancing Personalized Education for a Progressive Bharat 2047
Neethu Narayanan and
Daniel Madan Raja S
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Neethu Narayanan: Karunya Institute of Technology and Sciences
Daniel Madan Raja S: Karunya Institute of Technology and Sciences
Chapter 1 in Viksit Bharat 2047: Pathways to a Progessive Future, 2025, pp 1-9 from Shanlax Publications
Abstract:
With the establishment of well-rounded educational institutions, enormous opportunities open for leveraging the varied demographic challenges in India to provide equitable learning experiences for assorted ethnic, linguistic, and socioeconomic student groups. The paper investigates the inclusion of AI and ML in the development of adaptive learning systems and gamified platforms aimed at improving engagement, inspiration, and educational results. The proposed methodology aims to stimulate a revolved education pattern by introducing AI-based models to cater to individualized curriculum based on the pace and preferences of the learners while encompassing gamified aspects like incentives and interactive tasks. Key approaches consist of deep learning algorithms for local content customization, natural language processing for multilingual support, and predictive analytics for boosting gamification strategies. Preliminary results indicate that these systems meaningfully enhance students' performance and engagement when compared with more traditional approaches. This study highlights the significance of scalability and accessibility, especially in connection with how technological interventions can mitigate educational inequities in rural and underserved communities. This paper explicates the potential of AI and ML to transform the education ecosystem into one that is technically advanced, inclusive, and competency-oriented, in line with the vision of a progressive Bharat 2047, leaving future generations of children in India with a sun-soaked chance for a bright life.
Date: 2025
ISBN: 978-93-6163-772-8
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Persistent link: https://EconPapers.repec.org/RePEc:dax:vbpapf:978-93-6163-772-8:p:1-9
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