Yadav, Rohit, Yadav, Mohit
, Huzooree, Geshwaree
, Najeeb, Ali
and Ranasinghe, Asanga
(2025)
Accessibility and Inclusivity in AI-Powered Personalized Learning.
In:
Transformative AI Practices for Personalized Learning Strategies.
IGI Global, USA, pp. 47-67.
ISBN 9798369387443
![[thumbnail of Accessibility-and-Inclusivity-in-AI-Powered-Person.pdf]](https://pure.jgu.edu.in/style/images/fileicons/text.png)
Accessibility-and-Inclusivity-in-AI-Powered-Person.pdf - Published Version
Restricted to Repository staff only
Download (706kB) | Request a copy
Abstract
AI-powered personalized learning holds the potential to revolutionize education by tailoring content to meet the diverse needs of learners. However, for such systems to be truly effective, they must prioritize accessibility and inclusivity, ensuring equitable opportunities for all students, regardless of ability, background, or resources. This chapter explores the role of AI in personalized learning, focusing on the importance of designing systems that are both accessible and inclusive. It examines key issues such as algorithmic bias, data privacy, and the digital divide, offering strategies to mitigate these challenges. Additionally, it discusses the evolving regulatory landscape and the need for policies that foster equitable access to AI-driven educational tools. Looking ahead, the chapter highlights emerging innovations that promise to further enhance the accessibility and inclusivity of AI in education, ensuring that all learners can benefit from personalized, technology-enhanced learning experiences.
Item Type: | Book Section |
---|---|
Subjects: | Physical, Life and Health Sciences > Computer Science Social Sciences and humanities > Social Sciences > Education |
JGU School/Centre: | Jindal Global Business School |
Depositing User: | Mr. Luckey Pathan |
Date Deposited: | 12 Jun 2025 14:47 |
Last Modified: | 12 Jun 2025 15:02 |
Official URL: | https://www.igi-global.com/chapter/accessibility-a... |
URI: | https://pure.jgu.edu.in/id/eprint/9619 |
Downloads
Downloads per month over past year