This review analyzes the research trends in Artificial Intelligence in Education (AIED) from 2013 to 2023 using bibliometric analysis of 6843 publications from Web of Science and Scopus. The study reveals that China, the US, India, Spain, and Germany are the leading countries in AIED research. AIED research focuses more on higher education than K-12 education. Fifteen research trends were identified, including Educational Robots and Large Data Mining. Technologies such as machine learning, decision trees, deep learning, speech recognition, and computer vision are primarily used in AIED. Major implementations include educational robots, automated grading, recommender systems, learning analytics, and intelligent tutoring systems. Most AIED research is concentrated in seven major subject domains, with science, technology, engineering, and mathematics (STEM) and language disciplines being the most prominent, particularly computer science and English education. The Industrial Revolution 4.0 has accelerated the development of information technologies, with AI breakthroughs like Large Language Models and ChatGPT significantly impacting education. AI in education has provided opportunities to improve the quality and efficiency of education. However, technological implementations in education have lagged behind other areas like science and medical treatment. Education is a complex system, and technology alone cannot make substantial changes without professional learning, material development, and innovative pedagogies. The practical impact depends on educators and how they perceive and use technology in teaching. This study used bibliometric analysis to identify AIED research trends and how the field has evolved. Bibliometric analysis is a data-driven approach that allows for large-scale analysis of publications, offering a more comprehensive overview of existing literature. It provides an objective assessment of academic impact and relevance through quantitative metrics such as citation counts, h-index, and impact factors. The study focused on the past ten years due to the sharp increase in AIED research since 2013 and the dramatic increase in the applications of key AI technologies in education since 2013. The study aimed to answer three questions: (1) How diverse has AIED research been in terms of users, subject domains, and author geographies? (2) What are the research trends of AIED regarding technology, applications, and subject domain outcomes? (3) What are the research gaps in AIED?This review analyzes the research trends in Artificial Intelligence in Education (AIED) from 2013 to 2023 using bibliometric analysis of 6843 publications from Web of Science and Scopus. The study reveals that China, the US, India, Spain, and Germany are the leading countries in AIED research. AIED research focuses more on higher education than K-12 education. Fifteen research trends were identified, including Educational Robots and Large Data Mining. Technologies such as machine learning, decision trees, deep learning, speech recognition, and computer vision are primarily used in AIED. Major implementations include educational robots, automated grading, recommender systems, learning analytics, and intelligent tutoring systems. Most AIED research is concentrated in seven major subject domains, with science, technology, engineering, and mathematics (STEM) and language disciplines being the most prominent, particularly computer science and English education. The Industrial Revolution 4.0 has accelerated the development of information technologies, with AI breakthroughs like Large Language Models and ChatGPT significantly impacting education. AI in education has provided opportunities to improve the quality and efficiency of education. However, technological implementations in education have lagged behind other areas like science and medical treatment. Education is a complex system, and technology alone cannot make substantial changes without professional learning, material development, and innovative pedagogies. The practical impact depends on educators and how they perceive and use technology in teaching. This study used bibliometric analysis to identify AIED research trends and how the field has evolved. Bibliometric analysis is a data-driven approach that allows for large-scale analysis of publications, offering a more comprehensive overview of existing literature. It provides an objective assessment of academic impact and relevance through quantitative metrics such as citation counts, h-index, and impact factors. The study focused on the past ten years due to the sharp increase in AIED research since 2013 and the dramatic increase in the applications of key AI technologies in education since 2013. The study aimed to answer three questions: (1) How diverse has AIED research been in terms of users, subject domains, and author geographies? (2) What are the research trends of AIED regarding technology, applications, and subject domain outcomes? (3) What are the research gaps in AIED?