## 4 Results
### Future Directions of Research  
This section aims to address new areas of research in AI applied to EDM, ML with human intervention, and TA. After a thorough review of the literature and analysis of keywords, the main lines of research that are emerging in this area have been identified. Global research on this topic continues to evolve, integrating innovative concepts and approaches that open new possibilities for exploration. Based on the analysis of the most recent terms associated with this area, emerging research directions can be clearly identified. Cluster analysis is an effective tool for detecting emerging trends and problems in any scientific discipline. This process involves grouping the analysis units into clusters with similar elements, identifying the most recent terms through their relevance, and linking the terms found with emerging research areas. Table 2 presents the future research directions detected through the relevance score, whose detailed description is included below. This line of research refers to the use of big-data analytics to evaluate and improve educational systems, teaching methods, and student performance. This approach is based on collecting and analyzing data generated in educational contexts, such as attendance records, academic performance, interaction on digital learning platforms, participation in activities, and socio-emotional factors, among others. By applying big-data techniques, such as predictive analytics and ML, education leaders can identify patterns and trends that would not be evident with traditional evaluation methods [59]. In the educational area, big-data education evaluation allows for more effective personalization of learning. By analyzing data in real time, systems can recommend specific resources or teaching strategies tailored to the individual needs of each student. This is especially valuable for detecting students in time who might require additional support, allowing for personalized interventions that can improve their performance and engagement with learning [60]. In addition, this approach is also useful for evaluating the effectiveness of educational programs and methods. Institutions can review how certain methods impact learning, optimize resource use, and make evidence-based decisions. For example, by analyzing data on the use of a digital learning platform, one can identify which features of the platform contribute to student success and which may need improvement [61]. Likewise, analyzing big data allows educators and administrators to anticipate future trends and needs in education, facilitating more accurate strategic planning. In general terms, it promotes more inclusive and student-centered education, since data allows a better understanding of their needs and learning trajectories, as well as the factors that influence their success. With this, education becomes more efficient, personalized, and results-oriented, creating an educational system that responds more directly to the realities and challenges of each educational context. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Future Line of Research** & **Relevance Score** \\ \hline
1 & Big-Data Education Evaluation & 10.769 \\
2 & Multimodal Learning Analytic & 10.157 \\
3 & Latent Dirichlet Allocation & 10.037 \\
4 & Source Code Vulnerability Detection & 10.021 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Future research directions based on relevance score. Multimodal Learning Analytic  
Multimodal learning analytics is an emerging approach that uses data from multiple sources and modalities to analyze and better understand the learning process. This type of analysis leverages data from different channels, such as text, video, audio, interactions on digital platforms, gestures, movements, and even biometric records, thus combining multiple dimensions of information in the analysis of learning.