Abstract

The recent improvement of artificial intelligence (AI) in education has been a pathway to personalized learning, enabling individuals to learn independently with AI tools. AI-based platforms, such as adaptive learning and intelligent tutoring systems, offer significant opportunities to enhance access to education. These systems can improve learning in regions with limited resources and educational disparities. In developing nations, inadequate infrastructure, lack of digital resources, and socioeconomic barriers hinder formal education. AI-based platforms can help address these challenges and minimize disparities. The digital divide refers to limited digital device access and resource-driven inequalities, particularly affecting marginalized groups like low-income students, rural populations, and women in developing countries. AI-driven learning platforms can offer customized learning opportunities, fostering digital inclusion and minimizing the divide. These platforms are crucial for marginalized communities to develop digital literacy and compete in the digital world. Gamification, as an AI-based learning approach, can further enhance digital inclusion by making learning more engaging. However, implementing AI-driven learning platforms requires rigorous adaptation, especially in developing nations with diverse cultures and economic constraints. Research has predominantly focused on high-income countries where infrastructure is already established. This focus limits the application of AI-driven learning in developing nations, potentially excluding marginalized groups. Addressing this gap requires research on how AI tools can enhance educational infrastructure and culturally relevant learning in these regions. This study investigates the role of AI-based personalized learning systems in reducing the digital divide among marginalized groups in Bangladesh. It examines disparities in digital resource access, engagement with educational tools, and digital literacy development. Using metrics such as completion rates, task time, assessment scores, and user experiences, this study evaluates AI-driven learning platforms' effectiveness. Additionally, it explores socio-economic and cultural factors influencing adoption and effectiveness, addressing unequal digital access among low-income students and rural communities. This research aims to support inclusive education by demonstrating how AI-driven learning fosters digital literacy and equitable learning opportunities. By identifying necessary conditions for successful adoption, such as institutional support and accessibility, it offers valuable insights for educators, policymakers, and technologists. These findings can guide the development of sustainable AI applications, promoting digital inclusion and addressing educational inequalities.

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