This study explores the trends of chatbots in education studies by conducting a literature review to analyze relevant papers published in the Social Science Citation Index (SSCI) journals by searching the Web of Science (WoS) database. From the analysis results, it was found that the United States, Taiwan and Hong Kong are the top three contributing countries or regions. In addition, most studies adopted quantitative methods in their research design, such as ANOVA (Analysis of variance), descriptive statistics, t test, and correlation analysis. ANCOVA (Analysis of covariance) was the most frequently adopted approach for comparing the performances or perceptions of different groups of students. From the analysis results, the greatest proportion of studies adopted guided learning, followed by no learning activities. It was determined that the studies related to chatbots in education are still in an early stage since there are few empirical studies investigating the use of effective learning designs or learning strategies with chatbots. This implies much room for conducting relevant research to drive innovative teaching in terms of improving the learning process and learning outcomes. Finally, we highlight the research gaps and suggest several directions for future research based on the findings in the present study.

Learning mathematics has been considered as a great challenge for many students. The advancement of computer technologies, in particular, artificial intelligence (AI), provides an opportunity to cope with this problem by diagnosing individual students’ learning problems and providing personalized supports to maximize their learning performances in mathematics courses. However, there is a lack of reviews from diverse perspectives to help researchers, especially novices, gain a whole picture of the research of AI in mathematics education. To this end, this research aims to conduct a bibliometric mapping analysis and systematic review to explore the role and research trends of AI in mathematics education by searching for the relevant articles published in the quality journals indexed by the Social Sciences Citation Index (SSCI) from the Web of Science (WOS) database. Moreover, by referring to the technology-based learning model, several dimensions of AI in mathematics education research, such as the application domains, participants, research methods, adopted technologies, research issues and the roles of AI as well as the citation and co-citation relationships, are taken into account. Accordingly, the advancements of AI in mathematics education research are reported, and potential research topics for future research are recommended.