# The Integration of AI and Metaverse in Education: A Systematic Literature Review
## 4 Results and Discussion
### Rq4: What Are the Challenges of Utilizing AI in the Metaverse for Education?
#### 4.4.1 Technical Challenges  
The metaverse for education involves constant interactions between participants, which presents new scenarios for which AI needs to be adapted. However, current AI models are not yet specifically designed for such complex, interactive, and immersive virtual environments. Thus, it is challenging to create AI algorithms capable of effectively operating within the dynamic environments of the metaverse, due to requiring advanced algorithms capable of real-time adaptation and decision-making. A notable technical challenge is the refinement of AI-generated teaching videos from chatbot student interactions to create engaging educational content [56]. In fact, the work by [56] aimed to develop an AI generator capable of converting chatbot student dialogues into engaging educational videos to facilitate a more efficient student learning experience. Furthermore, AI algorithms should be able to understand and respond to human emotions, as well as social cues, in virtual contexts. These prove highly beneficial in educational settings because they enhance student engagement and interaction within the metaverse [106]. AI-based virtual assistants and chatbots play a pivotal role in enhancing interaction and engagement in the metaverse. Building on this, a significant challenge lies in developing virtual teaching assistants that are suitable for individual learners. This involves creating AI systems that can adapt to the unique learning styles and needs of each student, which requires sophisticated algorithms and a deep understanding of educational psy  
Figure 8: Key categories of challenges in integration AI within the metaverse for education. chology [6]. In addition, AI must also facilitate seamless communication between learners from different linguistic backgrounds. This requires advanced natural language processing capabilities to ensure that language barriers do not hinder the educational experience. Achieving this level of language processing is a significant technical challenge [6; 107]. Concerning practical application and real-world simulation, while AI can enhance interactivity, it cannot fully replicate real-world experiences. This limitation is particularly evident in fields that require hands-on practice, such as medical training, where AI-driven simulations may not capture all the nuances of real-world scenarios [108]. Tailored ML approaches are also necessary is in computer vision. Computer vision has the potential to significantly enhance the educational metaverse by enabling the analysis and recognition of human movements and actions. However, the specific environmental conditions of educational contexts, such as continuous interactions between teachers and students, necessitate the development of novel computer vision techniques and empirical investigations [54; 75]. These techniques should be designed to support the unique conditions of the metaverse, ensuring that they can effectively facilitate learning and engagement [54]. Ref. [75] discusses the potential of computer vision techniques within the educational metaverse but does not specify particular techniques in detail. Instead, it emphasizes the need for adapting existing computer vision methods to the unique conditions of the metaverse. Another critical area is the development of neural interfaces that allow seamless access to the metaverse. Current neural interfaces, such as XR headsets, can cause physical discomfort when used for extended periods, which is a significant concern in educational settings, because the usage can be sustained over an extended period. Tailored ML approaches could focus on optimizing these interfaces to minimize negative physical effects, such as headaches and eye strain [54]. Furthermore, AI techniques should be developed to address the psychological and social aspects of the educational metaverse. For instance, emotion recognition algorithms could be tailored to identify signs of psychological stress or depression, providing timely alerts to educators.