# The Integration of AI and Metaverse in Education: A Systematic Literature Review
## 4 Results and Discussion
### Rq3: What AI Techniques Are Utilized Within the Metaverse for Education?
Figure 5: AI techniques used in the metaverse for education. In Figure 6, we can observe that convolutional neural networks (CNNs) were cited in 15 papers, indicating their widespread application in educational contexts for image recognition and interactive content creation. Principal component analysis (PCA) was mentioned in seven papers; this performs a dimensionality reduction to facilitate the analysis of complex educational data. Generative Pre-trained Transformer (GPT) was found in five papers, highlighting the use of NLP capabilities for chatbots, content generation, and personalized learning. Decision trees were mentioned in four papers, as they can assist educators in making data-informed instructional choices. Long short-term memory (LSTM) was also cited in four papers, as it is effective for managing sequential data and tasks such as tracking student progress. K-means clustering, which appeared in four papers, is beneficial as a tool for segmenting students' needs or content as a way to tailor educational approaches. Logistic regression was mentioned in two papers, and is typically \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Definition** & **Reference** \\ \hline Decision trees & Decision trees are a non-parametric supervised learning method used for classification and regression by splitting the dataset into branches to form a tree-like model of decisions. & [84] \\ \hline Random forests & Random forests are an ensemble learning method that constructs multiple decision trees during training and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees. & [85] \\ \hline K-means clustering & K-means clustering is an unsupervised learning algorithm that partitions a dataset into K distinct clusters based on feature similarity, minimizing variance within each cluster. & [86] \\ \hline Hierarchical clustering & Hierarchical clustering is an unsupervised clustering technique that seeks to build a hierarchy of clusters by either a bottom-up (agglomerative) or top-down (divisive) approach. & [87] \\ \hline Principal component analysis (PCA) & PCA is a dimensionality reduction technique that transforms a dataset into a set of linearly uncorrelated variables (principal components) by identifying the directions of maximum variance. & [88] \\ \hline Q-learning & Q-learning is a model-free reinforcement learning algorithm that seeks to learn the value of an action in a particular state, allowing an agent to learn how to optimally act in a given environment. & [89] \\ \hline Deep Q network & DQN is a type of deep reinforcement learning algorithm that combines Q-learning with deep neural networks to approximate the Q-values, enabling the handling of high-dimensional state spaces. & [90] \\ \hline Convolutional neural networks & CNNs are a class of deep learning models primarily used for processing grid-like data, such as images, by employing convolutional layers to capture spatial hierarchies and local patterns. & [91] \\ \hline Recurrent neural networks & RNNs are a class of neural networks designed for processing sequences of data by using recurrent connections, allowing them to maintain information about previous inputs over time. & [92] \\ \hline Long short-term memory (LSTM) & LSTMs are a special kind of RNN designed to learn long-term dependencies by utilizing memory cells that can maintain information for long periods, effectively addressing the vanishing gradient problem. & [93] \\ \hline Fuzzy logic & Fuzzy logic is a form of many-valued logic that deals with reasoning that is approximate rather than precise, allowing for degrees of truth and enabling more flexible decision-making. Reference: [PERSON] (1965). “Fuzzy sets.” Information and Control. & [94] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._employed for binary classification tasks, such as predicting student success or dropout rates.