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<p>Generative AI (GenAI) tools have gained popularity across different learning and teaching contexts, becoming part of the mainstream in both classroom and online learning. This rapid AI adoption in education might help to provide students with personalized learning experiences that are not only intended to keep up with the pace of hybrid and Open and Distance Learning (ODL) but also envisioned to help students transcend their classroom and human teacher limitations. However, a result of the highly selective and rapidly growing technology access is what is commonly known as the digital divide that has socioeconomic status (SES) as one of its root causes. With the widespread GenAI adoption among ODL providers, studying SES as a potentially driving force of the GenAI adoption in SDL could help to identify students who are not benefiting from AI advances in education and further consider ways to narrow the technology-driven learning inequalities. This study reports on the effects of three self-directed learning (SDL) attributes—self-management, self-monitoring, and motivation—on students’ intention to use GenAI tools in SDL and how these relationships are moderated by students’ SES. The study sample of 171 undergraduate students was drawn from four universities in Malaysia. Partial Least Squares Structural Equation Modeling (PLS-SEM) is used for data analysis. The results indicate that the moderation of the relationships between self-management, self-monitoring, and motivation and students’ intention to use GenAI tools by their SES was significant. The findings of this study could indicate that students with better access to internet connection, digital devices, and AI literacy (characteristics of higher SES) are more likely to engage in GenAI use in SDL. The inaccessibility of using AI tools in learning to students with low SES could have a detrimental effect and further divide students by access to technology and digital resources. In this way, the results of this study can help identify the potentially less-privileged group of students and indicate the need for closing the digital divide and making AI-augmented learning tools more accessible to everyone, in alignment with United Nations Sustainable Development Goal 4 (SDG 4).</p>

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Bridging the Digital Divide: Socioeconomic Equity in AI-Assisted Hybrid Learning

  • 期刊论文

  • 发表日期：

    2025年12月26日

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  • Racheal Poh,
  • Bee Wah Tan,
  • Heng Wei Lee,
  • Kian Lam Tan

摘要

Generative AI (GenAI) tools have gained popularity across different learning and teaching contexts, becoming part of the mainstream in both classroom and online learning. This rapid AI adoption in education might help to provide students with personalized learning experiences that are not only intended to keep up with the pace of hybrid and Open and Distance Learning (ODL) but also envisioned to help students transcend their classroom and human teacher limitations. However, a result of the highly selective and rapidly growing technology access is what is commonly known as the digital divide that has socioeconomic status (SES) as one of its root causes. With the widespread GenAI adoption among ODL providers, studying SES as a potentially driving force of the GenAI adoption in SDL could help to identify students who are not benefiting from AI advances in education and further consider ways to narrow the technology-driven learning inequalities. This study reports on the effects of three self-directed learning (SDL) attributes—self-management, self-monitoring, and motivation—on students’ intention to use GenAI tools in SDL and how these relationships are moderated by students’ SES. The study sample of 171 undergraduate students was drawn from four universities in Malaysia. Partial Least Squares Structural Equation Modeling (PLS-SEM) is used for data analysis. The results indicate that the moderation of the relationships between self-management, self-monitoring, and motivation and students’ intention to use GenAI tools by their SES was significant. The findings of this study could indicate that students with better access to internet connection, digital devices, and AI literacy (characteristics of higher SES) are more likely to engage in GenAI use in SDL. The inaccessibility of using AI tools in learning to students with low SES could have a detrimental effect and further divide students by access to technology and digital resources. In this way, the results of this study can help identify the potentially less-privileged group of students and indicate the need for closing the digital divide and making AI-augmented learning tools more accessible to everyone, in alignment with United Nations Sustainable Development Goal 4 (SDG 4).