## 3 Materials and Methods
While quantitative metrics can show how many studies focus on topics such as EDM or HITL, they do not reflect the complexity of how educators and students interact with these technologies in real learning situations. For example, how educators customize the use of AI to fit the needs of each student or how students perceive and benefit from these technologies. Furthermore, aspects such as creativity in the design of automatic teaching systems and ethical challenges related to the integration of AI in the classroom, such as privacy and equity, are often not fully reflected in bibliometric analysis. These issues require a qualitative approach that captures the experiences, concerns, and opinions of those involved in the educational process. To address this limitation, future research could find it useful to incorporate qualitative or content analysis methods, such as interviews with researchers or educators, to gain a deeper understanding of the implications of these technological advances in education. To identify current and emerging research lines on emotional creativity in art education, an analysis of keyword co-occurrences from the sample documents was conducted using the specific terminology of the VOSviewer tool. Key concepts include links, which represent the co-occurrence between keywords, and total link strength, which measures the number of documents in which two keywords appear together and the numerical strength of these links. Clusters, defined as groups of keywords within a network map, and network maps, comprising keywords and their links, were also central to the analysis. It is important to note that clusters do not necessarily encompass all components of a network map. Additionally, the attributes used to describe keywords were \"weight\" and \"score\". The weight of a keyword indicates its significance within the research field. Specifically, link weight reflects the number of connections a keyword has with other terms, while link strength weight reveals the total strength of these connections [35]. The score attribute ranks keywords in the titles and abstracts of the papers by relevance, with higher scores serving as better predictors for identifying future research lines. \[Relevance\ score\ =\ \frac{n_{ax}}{n_{bx}+c},\]  
The relationship examines the frequency of keyword x in areas a and b, using the parameters n\({}_{ax}\) and n\({}_{bx}\) to denote the number of elements where x appears. It balances the frequency of x in area a (parameter c) relative to its frequency in area b and considers its absolute frequency in the area as an indicator of its relevance to area a. Another aspect that limits the scope of the study is that only articles published in scientific journals indexed in Scopus have been considered, which excludes a large amount of research that could have been published in other formats, such as books, technical reports, or conference proceedings. The inclusion of these other types of publications could offer a more complete view of emerging trends in educational technologies. The study also recognizes that the evolution of this research is not a linear or homogeneous process. The trends observed in the literature could be influenced by external factors, such as changes in educational policies, advances in information and communication technologies, or even global events such as the COVID-19 pandemic. Therefore, in future research it could be valuable to incorporate a more contextual approach.