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Generative AI for Personalized Learning Content Creation

Yadav, Rohit; Huzooree, Geshwaree; Yadav, Mohit; Gangodawilage, Damith Sanjaya Kumara

Authors

Rohit Yadav

Mohit Yadav

Damith Sanjaya Kumara Gangodawilage



Contributors

Lydia Kyei-Blankson
Editor

Esther Ntuli
Editor

Abstract

Generative AI has emerged as a transformative force in personalized learning, offering unprecedented opportunities to tailor educational content to individual needs. By leveraging advanced algorithms and data analysis, AI systems can dynamically generate customized materials, provide adaptive feedback, and foster student engagement. This chapter explores the intersection of generative AI and personalized learning, discussing its techniques, tools, and applications in creating immersive and adaptive educational experiences. Key benefits include enhanced learning outcomes, efficiency, and scalability. However, challenges such as data privacy, algorithmic bias, and equitable access must be addressed to ensure responsible implementation. Future trends, including the integration of immersive technologies like Virtual Reality (VR) and predictive analytics, highlight AI's potential to revolutionize education. By navigating ethical considerations and fostering transparency, generative AI can become a powerful ally in creating inclusive, engaging, and student-centered learning environments.

Citation

Yadav, R., Huzooree, G., Yadav, M., & Gangodawilage, D. S. K. (2025). Generative AI for Personalized Learning Content Creation. In L. Kyei-Blankson, & E. Ntuli (Eds.), Transformative AI Practices for Personalized Learning Strategies (107-130). IGI Global. https://doi.org/10.4018/979-8-3693-8744-3.ch005

Publication Date Apr 25, 2025
Deposit Date Apr 30, 2025
Publisher IGI Global
Pages 107-130
Series Title Advances in Computational Intelligence and Robotics
Book Title Transformative AI Practices for Personalized Learning Strategies
ISBN 9798369387443
DOI https://doi.org/10.4018/979-8-3693-8744-3.ch005
Public URL http://researchrepository.napier.ac.uk/Output/4245954