# 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?  
AI is crucial for enhancing the educational experience within the metaverse by providing tools including virtual teaching assistants, advanced language processing capabilities,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**AI Algorithm** & **Purposes** & **Proposals** \\ \hline Linear Regression, & & \\ Logistic Regression & Training a machine learning model on pre-processed data. & [95; 96] \\ \hline Support Vector Machines & Classification recognition for digital twin technology. & [97] \\ \hline Decision Trees & (i) Forecasting how parameters in an algorithm will be correlated for machine learning; (ii) mimicking learner reactions that resemble those of a human. & [63; 67; 95; 98] \\ \hline K-Means Clustering & Improving the route for the transmission of 360-degree immersive VR football instructional films and determining the position for the closest proxy server for each participant. & [99; 100] \\ \hline Principal Component Analysis & (i) Integrating VR with machine learning to examine the accuracy of the trained algorithm; (ii) identifying high-level properties from raw eye tracker data. & [60; 69; 99] \\ \hline Q-Learning, & & \\ Hierarchical Reinforcement Learning & Increasing the intelligence of non-player characters. & [63] \\ \hline Convolutional Neural Networks & (i) Understanding speech executor; (ii) detecting emotion and offering recommendations regarding teaching methods; (iii) simulating how the nerve that supplies vision in humans interprets visual data; (iv) rating the second generation of web technology’s performance in the football classroom. & [48; 63; 68; 96; 100; 101] \\ \hline Recurrent Neural Networks & Understanding of context-aware speech in aircraft training and education. & [101] \\ \hline Long Short-Term Memory & Connecting VR to machine learning. & [99] \\ \hline Fuzzy Logic & (i) Incorporating human subjective cognition into the Item Response Theory (IRT) learning system; (ii) developing a better and captivating classroom for learners; (iii) providing a simple solution resembling GUI. & [60; 102] \\ \hline NLP & (i) Constructing a ubiquitous operating system model for the metaverse; (ii) examining the potential impact of chatbots on managerial training; (iii) studying how AI chatbots within virtual world settings affect preliminary English instructors; (iv) examining the impacts of using chatbots within the metaverse for education. & [51; 56; 103; 104; 105] \\ \hline \hline \end{tabular}
\end{table}
Table 5: AI algorithms in the metaverse for education.  
and sophisticated learning outcome assessments. However, it is vital to continue to recognize the challenges faced when integrating AI within the metaverse, particularly as the metaverse development for education is currently at an early stage, and there remains a lack of comprehensive studies. The aim of this question is to prompt an in-depth investigation to establish the challenges associated with the use of AI within the metaverse for education, which opens a new axis for ongoing research in this field. The review of the sixty-two eligible papers identified several challenges, which we classified into five categories, discussed in detail below, and consisting of (1) technical challenges (N = 7); (2) algorithm bias (N = 3); (3) ethical and security concerns (N = 6); (4) integration and interoperability challenges (N = 4); and (5) computational and hardware challenges (N = 4). In Figure 8, we summarize the five key categories of challenges along with their respective percentages. The distribution highlights the complexity and multifaceted nature of AI deployment in this innovative educational environment.