Artificial Intelligence in Education: Trends, Challenges, and Research Gaps A Systematic Review

Abstract

Artificial Intelligence (AI) has become one of the revolutionizing trends in the sphere of education, altering the process of teaching, learning, and assessing, as well as managing institutions. The systematic review is a synthesis of the current body of literature to review significant trends, common challenges, and knowledge gaps in AI-driven education. Based on the recent peer-reviewed literature, policy reports, and empirical research, the review will classify AI applications into adaptive learning systems, intelligent tutoring systems, automated assessment, learning analytics, generative AI tools, and administrative automation. Although AI proves to have significant potential in the areas of personalization, engagement, and efficiency, it also poses threats to ethics, bias, data privacy, academic integrity, teacher preparedness, and digital inequity. Research methodological weaknesses in literature that are identified in the study are short-term assessments, lack of cross-cultural validation, and has inadequate theoretical basis. Lastly, the paper also specifies future research directions, including responsible AI frameworks, inclusive design, longitudinal impact studies, and policy integration in line with Education 5.0. Interdisciplinary research of gaps in AI in education is a promise of its future as well as technological innovation. A shift to long-term, theory-oriented, and ethics-driven research will make sure that AI will be a valuable addition to the process of sustainable and humanistic educational change.