This study explores the roles and research foci of AILEd (Artificial Intelligence in Language Education). The AILEd studies published from 1990 to 2020 in the WOS (Web of Science) database were included in the present study. Based on the well-recognized Technology-based Learning Review model, several dimensions, such as research methods, research sample groups, adopted technology, language skills, the role of AI in language education, and learning outcomes, were taken into account. The review results show that the main application domains of AILEd research were writing, reading, and vocabulary acquisition. In terms of applied technology and algorithms, AI in language education mostly adopted ITS (Intelligent Tutoring System) and NLP (Natural Language Processing). Besides, several commonly used AI algorithms were Statistical Learning, Data Mining, Machine Learning, and Natural Language Parsing. It was also found that some research focused on learning anxiety, willingness to communicate, knowledge acquisition, and classroom interaction. However, higher order thinking, complex problem solving, critical thinking ability, and collaborative learning tendencies were rarely considered in AILEd studies. Accordingly, several suggestions are provided to researchers, teachers, and decision makers for applying or studying AI applications in language education in the future.

Fostering students’ competence in applying interdisciplinary knowledge to solve problems has been recognized as an important and challenging issue globally. This is why STEM (Science, Technology, Engineering, Mathematics) education has been emphasized at all levels in schools. Meanwhile, the use of robotics has played an important role in STEM learning design. The purpose of this study was to fill a gap in the current review of research on Robotics-based STEM (R-STEM) education by systematically reviewing existing research in this area. This systematic review examined the role of robotics and research trends in STEM education. A total of 39 articles published between 2012 and 2021 were analyzed. The review indicated that R-STEM education studies were mostly conducted in the United States and mainly in K-12 schools. Learner and teacher perceptions were the most popular research focus in these studies which applied robots. LEGO was the most used tool to accomplish the learning objectives. In terms of application, Technology (programming) was the predominant robotics-based STEM discipline in the R-STEM studies. Moreover, project-based learning (PBL) was the most frequently employed learning strategy in robotics-related STEM research. In addition, STEM learning and transferable skills were the most popular educational goals when applying robotics. Based on the findings, several implications and recommendations to researchers and practitioners are proposed.

English speaking is a challenging learning objective for EFL (English as Foreign Language) learners owing to the lack of practicing environments. In this study, Interactive Spherical Video-based Virtual Reality (ISVVR) was adopted as the learning mode to provide realistic sociocultural contexts for English speaking. In addition, an experiment was conducted to investigate the effects of the SVVR approach on the English speaking performances and anxiety of the EFL students with different cognitive styles. A pretest-posttest quasi-experimental design was conducted to investigate the effects of the interaction between cognitive style and multimedia learning mode on students’ learning motivation, speaking anxiety, oral presentation, and move analysis. The student sample consisted of two classes of 93 students. All of the students took the Group Embedded Figures Test (GEFT) to identify whether their cognitive style was field dependent or independent. The results showed a significant effect for the interaction between the cognitive styles and the learning modes on the students’ oral presentation and speaking anxiety. Their structural moves in oral presentations with the ISVVR learning mode showed more similar spoken genre patterns to those of TED speakers. Findings further support the benefit of incorporating cognitive styles into the ISVVR learning mode for EFL learners.