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.

Artificial intelligence (AI) has been widely explored across the world over the past decades. A particularly emerging topic is the application of AI in e-learning (AIeL) to improve the effectiveness of teaching and learning in precision education. This study aims to systematically review publication patterns for AIeL research with a focus on leading journals, countries, disciplines, and applications. In addition, a co-citation network analysis was conducted to explore the invisible relationships among the core papers of AIeL to reveal directions for future research. The analysis is based on a total of 86 core AIeL papers accompanied by 1149 citations in follow-up studies obtained from the Web of Science. It was found that a majority of AIeL studies focused on the development and applications of intelligent tutoring systems, followed by using AI to facilitate assessment and evaluation in e-learning contexts. For field researchers, the visualized network diagram serves as a map to explore the invisible relationships among the core AIeL research, providing a structural understanding of AI-supported research in e-learning contexts. A further investigation of the follow-up studies behind the highly co-cited links revealed the extended research directions from the AIeL mainstreams, such as adaptive learning-based evaluation environments. Implications are discussed.

The childbirth education training course aims to help nurses establish the basic ability to care for pregnant women. However, in traditional teaching, it is often difficult for learners to distinguish the meaning of different childbirth signs. While virtual reality (VR) may be connected in most people’s minds with videos designed for immersive entertainment, in fact its early purpose was for nursing education. Initial forms of Spherical Video-based Virtual Reality (SVVR) were used to prepare and train nursing students, pilots, and military personnel. In this paper, an exploratory study using social learning theory embedded in SVVR for childbirth education training is presented; moreover, the learning performances of nursing students who participated in SVVR classroom learning and those who learned with the traditional approach were compared. The experimental results show that, compared with the traditional instruction, the learning motivation and learning satisfaction of the students who learned with SVVR were better, showing the potential of this powerful medium for enhancing nursing students’ learning performance in the context of childbirth education.