The Evolving Landscape of AI in Education: A Systematic Review of Contemporary Research (2024-2025)
Abstract
Artificial Intelligence (AI) is increasingly reshaping educational systems worldwide, influencing pedagogy, assessment, governance, and learner engagement. While the volume of AI-in-education research has expanded rapidly, particularly following the emergence of generative AI, there remains a need for up-to-date synthesis focused on the most recent phase of adoption. This study presents a systematic review of peer-reviewed research on AI in education published between 2024 and 2025, conducted in accordance with the PRISMA 2020 guidelines. A comprehensive search of Scopus, Web of Science, IEEE Xplore, and the ACM Digital Library yielded 37 eligible studies. Using a narrative synthesis approach, the review examines publication trends, research methods, thematic focus areas, and educational contexts. The findings reveal a field dominated by meta-research, including bibliometric analyses, conceptual papers, and systematic reviews, with limited primary empirical evidence assessing realworld educational outcomes. Major thematic areas include personalized and self-regulated learning, generative AI and assessment, AI literacy, ethical and equity concerns, and institutional governance. Higher education and professional training contexts are disproportionately represented, while school-level education receives comparatively limited attention. The review highlights a growing research–practice gap, as technological adoption outpaces empirical validation and policy development. Overall, the study provides a timely synthesis of contemporary AI-in-education research, identifying critical evidence gaps and governance challenges. It underscores the need for empirically grounded, ethically informed, and context-sensitive approaches to AI integration that move beyond conceptual promise toward sustainable and equitable educational practice