From AI innovation to educational inclusion: comparative perspectives on AI-enabled personalized learning and transformative skill development across developed and developing country contexts

The integration of Artificial Intelligence (AI) in higher education offers significant potential to enhance personalized learning and workforce development. However, AI adoption varies widely between developed and developing countries due to differences in digital infrastructure, socio-economic conditions, and institutional capacity. This study employs a meta-synthesis approach following PRISMA guidelines to systematically synthesize empirical studies on AI’s role in reshaping personalized learning and skill development in higher education across developing and developed country contexts. The study findings reveal that while AI can improve educational outcomes globally, equitable benefits depend on context-sensitive implementation. In developed countries, AI integration in higher education has advanced to an innovation–optimization phase, featuring adaptive learning platforms, generative instructional tools, and strong governance structures. Despite these advancements, challenges remain around algorithmic transparency, pedagogical integration, and maintaining human agency. In contrast, developing countries are often in an access–readiness phase, where AI is primarily considered for expanding access, addressing resource limitations, and fostering basic digital literacy. Barriers such as infrastructure deficits, low AI literacy, and reliance on external algorithms hinder equitable adoption and localization efforts. The study highlights the contrasting challenges faced by each context: developed regions focus on ethical oversight amid innovation, while developing regions struggle with foundational capacity and sustainability. Based on these insights, an equity-driven framework for inclusive AI integration is proposed, centered on four pillars: governance and policy alignment, infrastructure and access equity, algorithmic transparency, and AI literacy. This framework aims to guide institutions in aligning AI adoption with local realities and promoting inclusive and sustainable education.