## 4 Results
### Keyword Analysis  
To analyse the keywords in the selected articles, we first carried out a standardisation process based on the following criteria: (a) standardising words in singular and plural (e.g., intelligence and intelligences); (b) unifying synonyms in the same concept and purifying terms to avoid potential semantic conflicts or conceptual redundancies; (c) eliminating neutral words not directly related to the topic, such as \"article\", but keeping others that in their context did have a related meaning such as \"cognition\" [66]. Once the process of standardising and debugging the keywords had been completed, the final list was extracted using the Vosviewer software [38], and the keywords are listed according to their relevance in the light of their frequency/presence in the corpus (Table 5). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Keywords** & **Occurences** & **Total Link Strength** \\ \hline
1 & Learning & 13 & 55 \\
2 & Attention & 6 & 44 \\
3 & Indicator recognition & 6 & 42 \\
4 & Intelligence & 6 & 27 \\
5 & Artificial intelligence & 5 & 35 \\
6 & Student behaviours & 5 & 37 \\
7 & Education & 4 & 34 \\
8 & Adaptive learning & 3 & 24 \\
9 & Data analytics & 3 & 23 \\
10 & Machine learning & 3 & 26 \\
11 & Online learning & 3 & 25 \\
12 & Behavioural research & 2 & 15 \\
13 & Cognition & 2 & 12 \\
14 & Computer vision & 2 & 10 \\
15 & Electroencephalography & 2 & 11 \\
16 & Emotions & 2 & 8 \\
17 & Human computer interaction & 2 & 17 \\
18 & Privacy protection methods & 2 & 15 \\
19 & Teaching & 2 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Frequency of topics related to artificial intelligence, education, and level of attention or concentration [38]. Figure 4: List of principal journals. The item \"learning\" clearly stands out among the main keywords in the analysis, given that the main objective of the use of AI in the studies was to improve learning. In second place comes the term \"attention\", which highlights its importance in the studies analysed. This is followed by the item \"indicator recognition\", which brings together various keywords in the standardisation process and refers to the measurement and recognition of various aspects and indicators for subsequent analysis, such as facial or postural recognition, gaze, actions, etc. \"Intelligence\" and \"artificial intelligence\", in fourth and fifth place, are key terms for the analysis insofar as their use formed part of the methodology of the research selected. \"Student behaviour\" is also shown to be a relevant term, being the object of the research analysed. \"Adaptive learning\" is also prominent, as the aim of the studies was to improve learning systems based on measurements of students' attention, with \"data analysis\" being central to their methodology. The extracted keywords can be contextualised using Figure 5, which illustrates their co-occurrence network, underlining both their incidence and interrelationships [67; 68]. This analysis uses 30 nodes with their corresponding terms extracted from the titles, keywords, and abstracts of the articles selected from the Wos, Scopus, and APA PsyInfo databases. As a result, four distinct clusters were established (Figure 5). The first cluster, in blue, refers to artificial intelligence and several related technologies used in studies, such as xgboost [58], as well as online and adaptive learning. On the other hand, Cluster 2, in yellow, shows the relationship between the concepts of learning and attention, as well as the terms game-based learning and one of the techniques used in several studies to measure the level of attention, namely electroencephalography [42; 52]. The green cluster shows the relationship between the concept of intelligence and the recognition of attention indicators, with cognition, theoretical models, teaching, machine learning, and the human-computer relationship. Finally, the red cluster relates student  
Figure 5: Keyword co-occurrence network with 30 items, created using software Vosviewer [42].