## 4 Discussion
### Thematic, Trend, and Topic Analysis  
In addition to the analysis of the documents presented, to better understand the main topics and themes examined in the literature, the documents' keywords were also examined. Both author's keywords and keywords plus/index keywords were used since they can effectively represent the documents' knowledge structure [100]. Specifically, the most commonly used keywords plus/index keywords were as follows: intelligent tutoring systems (\(n=22\)), augmented reality (\(n=17\)), virtual reality (\(n=10\)), computer-aided instruction (\(n=9\)), artificial intelligence (\(n=8\)), education (\(n=6\)), simulation (\(n=6\)), computing education (\(n=5\)), design (\(n=5\)), human-computer interaction (\(n=5\)), immersive learning (\(n=5\)), learning systems (\(n=5\)), personalized learning (\(n=5\)), and students (\(n=5\)). While the most frequently used author's keywords were as follows: augmented reality (\(n=18\)), intelligent tutoring systems (\(n=18\)), virtual reality (\(n=8\)), artificial intelligence (\(n=7\)), human-computer interaction (\(n=6\)), training (\(n=6\)), education (\(n=5\)), personalized learning (\(n=4\)), adaptive learning (\(n=3\)), surgery (\(n=3\)), design (\(n=2\)), immersive learning (\(n=2\)), simulation (\(n=2\)), and teachers (\(n=2\)). These outcomes were further explored through a co-occurrence network analysis using both Bibliometrix and VOSviewer. In the case of Bibliometrix, keywords plus/index keywords were used while for VOSviewer, both author's keywords and keywords plus/index keywords were used. The related networks are presented in Figures 5 and 6, respectively. Specifically, in the Bibliometrix co-occurrence network, ten clusters arose. The clusters and related keywords identified were as follows: Blue cluster (\(n=14\) keywords)--computer-aided instruction, virtual reality, learning systems, artificial intelligence, tutoring systems, adaptive learning, education, computing education, e-learning, intelligent tutoring, personalized learning, immersive learning, learning experiences, and engineering education; Red cluster (\(n\) = 3 keywords)--intelligent tutoring systems, augmented reality, and human-computer interaction; Green cluster (\(n\) = 2 keywords)--teaching and learning. The following seven single-keyword clusters were also identified: architecture (Gray cluster); design (Brown cluster); simulation (Orange cluster); students (Pink cluster); surgery (Turquoise cluster); teachers (Purple cluster); and training (Lavender gray cluster). Figure 5: Co-occurrence network—Bibliometricx. Figure 6: Keyword co-occurrence network—VOSviewer. Furthermore, the co-occurrence network of VOSviewer resulted in the creation of three clusters. The first cluster (Red) was related to adaptive learning, computer-aided instruction, computing education, e-learning, engineering education, immersive learning, intelligent tutoring, learning experiences, learning systems, personalized learning, tutoring systems, and virtual reality. The second cluster (Green) was related to architecture, artificial intelligence, design, environments, framework, human-computer interaction, intelligent tutors, students, teachers, and training. The third cluster (Blue) was related to augmented reality, education, head-mounted displays, intelligent tutoring systems, learning, simulation, surgery, and teaching. Additionally, Table 4 presents the total link strength of the related keywords. Based on the outcomes, intelligent tutoring systems, augmented reality, computer-aided instruction, and virtual reality had the highest total link strength. These outcomes are also in line with the trend topics identified, which are displayed in Figure 7. Specifically, an emphasis on computer-aided instruction, computing education, and simulations can be observed. Emphasis was placed on both augmented reality and virtual reality; however, the integration of intelligent tutoring systems into extended reality technologies initially focused on augmented reality technology.