From Promise to Practice: Systemic Factors Influencing AI Adoption in Higher Education

Abstract

This study explores the integration of artificial intelligence (AI) in adaptive learning within higher education, focusing on its effectiveness, challenges, and strategic implementation. The objective is to assess how AI-driven technologies—such as machine learning, natural language processing, and learning analytics—support personalized education and improve student outcomes. The methodology involved a narrative review of peer-reviewed literature sourced from Scopus, PubMed, and Google Scholar, using a targeted Boolean search strategy and strict inclusion criteria. Studies were selected based on their empirical focus, educational context, and relevance to AI-enabled adaptive learning. The findings reveal that AI technologies significantly enhance student engagement and academic performance by tailoring content delivery, monitoring progress, and enabling real-time feedback. However, institutional readiness varies greatly between developed and developing countries. While well-resourced institutions have successfully embedded AI into their pedagogical systems, many universities in Southeast Asia struggle with limited infrastructure, faculty preparedness, and policy support. Systemic barriers—such as lack of funding, inadequate infrastructure, and insufficient training—emerge as critical challenges. To overcome these barriers, the study suggests coordinated policy efforts, investment in digital infrastructure, faculty training, and inclusive design approaches. Future research should address the long-term impacts of AI in education and ethical considerations related to data use. These efforts are essential to ensure equitable, effective, and sustainable AI adoption that can transform higher education globally.

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