## 3 Result Analysis
### Theoretical and Review Study Analysis  
Of the 32 studies, 9 were categorized as Theoretical and Review studies [42; 62; 63; 64; 65; 66; 67; 68; 69]. These studies have contributed to the field through the exploration of the topic and the development of frameworks, architectures, and guidelines. However, these studies did not involve the development and presentation of an application or system, nor did they involve the conducting of a case study within educational settings. [PERSON] [42] explored the impact of combining augmented reality with artificial intelligence to create immersive intelligent tutoring systems. Specifically, the study presented the integration of these technologies in educational settings and commented upon the potential that their combination can bring about in teaching and learning practices. Finally, the study highlighted the ability of such systems to consider learners' characteristics, preferences, as well as their motivational, cognitive, and psychological states and to be applied across educational levels. [PERSON] et al. [62] focused on integrating deep learning techniques into augmented reality and virtual reality experiences to create effective intelligent tutoring systems that aid students in surgical training and assessment. They highlighted personalization, high realism, and real-time feedback as integral parts of such systems. The study also discussed the benefits and challenges of integrating deep learning techniques to create adaptive training experiences and presented examples of intelligent tutoring systems that were successfully developed and introduced in the field of surgical training. Finally, the study commented upon the rise of robotics and data-driven initiatives to offer tailored training programs to increase the effectiveness of skill acquisition and development. [PERSON] et al. [63; 64] presented a framework for creating intelligent virtual reality tutoring systems in the context of engineering education. Their approach used three core modules, capitalized on the semantic web, and focused on reducing the high cost of setting up and maintaining virtual reality environments. Specifically, they stated that by using the semantic web, it is feasible to use its reasoning capabilities and heterogeneous knowledge integration to facilitate virtual reality training material creation, maintenance, and update. [PERSON] et al. [65] showcased their proposed architecture to effectively combine intelligent tutoring systems with augmented reality. Emphasis was put on the analysis of existing augmented reality and intelligent tutoring systems and the potential benefits that can be yielded through their combination. They highlighted the need to focus on extensibility, interoperability, low latency, and low response time to enable such systems to be effectively used in education. Finally, they emphasized real-time content and information generation. [PERSON] and [PERSON] [66] explored the convergence of various educational technologies. In their study, they commented upon the capabilities of artificial intelligence and its potential to be used within augmented reality and virtual reality environments to create effective intelligent tutoring systems. They also highlighted that through this approach, students' adaptive and personalized learning can be enriched. Finally, they commented upon the need to develop new educational frameworks that capitalize on such intelligent tutoring systems. [PERSON] et al. [67] presented a framework to develop artificial intelligence-based immersive learning to support learners' hard-skills training. Specifically, they focused on integrating intelligent tutoring systems into virtual reality environments to create virtual reality-native intelligent tutors. The study highlighted the need to focus on learners' cognition and on increasing their bodily engagement by supporting embodied learning.