Asian Journal of Education and Social Studies

Artificial Intelligence (AI) emerges as a critical technological intervention that promises to revolutionize educational approaches, pedagogical methodologies, and learning experiences. Traditional educational models are increasingly becoming inadequate in addressing the complex, dynamic skills requirements of a rapidly evolving global knowledge economy. The integration of AI technologies represents a pivotal mechanism for reimagining educational delivery, personalization, and strategic innovation. This systematic review examines the conceptual structure of artificial intelligence in education (AIED) research, focusing on AI applications, research topics, and research design elements. Following PRISMA guidelines, the study analyzed peer-reviewed articles published between 2020 and 2024 from major academic databases. The PRISMA methodology consists of a 27-item checklist and guidelines to enhance the quality of reporting as well as the credibility of systematic reviews. The findings reveal that AI applications in education primarily cluster into four categories: Emerging technologies, intelligent evaluation and management, personalized tutoring and adaptive learning, and profiling and prediction. The investigation of research topics indicates a strong emphasis on system and application design, followed by studies on the adoption and acceptance, impacts, and challenges of AIED. Analysis of research designs shows that while descriptive and survey methods dominate the field, there is limited use of experimental and mixed methods approaches. The theoretical foundations of AIED research demonstrate its multidisciplinary nature, drawing from fields such as education, psychology, mathematics, and sociology, though many studies lack robust theoretical grounding. Research contexts predominantly focus on higher education and K12 settings, with minimal attention to preschool education. This review contributes to the understanding of AIED's current landscape and suggests future research directions, including the need to incorporate emerging AI technologies, strengthen research in underrepresented educational contexts, enhance methodological rigour, deepen theoretical contributions, and foster interdisciplinary collaboration. These insights are particularly valuable for educational stakeholders navigating the transformation toward Education 4.0 and beyond.