## 3 Materials and Methods
### Method of Analysis  
According to [PERSON] et al. [10], there are two main procedures in bibliometric analysis: performance analysis and science mapping.  
While the former aims to evaluate the impact of the activity of authors, institutions, countries, etc., the second is focused on displaying the structure and dynamics of the research on one specific area, according to [PERSON] et al. [33].  
A performance analysis is usually composed of the number of publications, the number of citations, and an index based on both numbers, the h-Index [34]. Although many authors have reviewed the h-Index and proposed alternatives ([35; 36; 37; 38], among others), this index offers a high value and is easy to obtain, so it is used very frequently in bibliometric performance analysis.  
We discarded the use of the g-Index in our bibliographic analysis for several reasons: we saw few differences in using this index compared with the use of the h-Index (no new entrants nor items exiting from the tables). This can be due to the generally low number of citations [39], as this topic is very new, and the g-Index is not an official number given by WoS. Thus, the process of calculations could introduce undesired errors in our analysis due to authors using different names, the absence of ORCID ID in some cases, etc.  
Based on the previous considerations, this paper shows the journals ordered according to the h-Index computed based on the author's number of existing publications and citations in the WoSCC dataset (h); second, by the number of citations (TC); and third, by total scientific production in AI (TPAI).  
In the case of the most productive and influential journals, this paper combines these indexes with the impact factor (IF) for 2023 (the last year published at the time we wrote  
Figure 1: Flowchart of the bibliometric research strategy.  
this paper), and the last 5-year impact factor (5-IF) to provide a complete picture of each included source.  
Regarding the science mapping techniques, these are aimed at monitoring a research field to define its cognitive structure, that is, the structure of relationships between keywords, authors, etc., and its evolution in specific periods [10].  
For the selected period, we analyzed all results from the Web of Science Core Collection (WoSCC) spanning from 1997 to 2024, the time at which this analysis was conducted. Upon examining the evolution of published material, we observed a significant increase starting in 2018. Furthermore, we chose to specifically analyze the period between the substantial rise of large language model-based chatbots (2021) and the last year analyzed. Consequently, we divided the dataset into two distinct periods (pre-2018 and post-2018) and conducted a detailed examination of the years 2021-2024 for a specific inquiry.  
This research used a suite of software tools for comprehensive quantitative analysis and visualization of the collected literature. These tools included R-version 4.4.1 combined with Bibliometrix and Biblioshiny [40], VOSviewer 1.6.20, SCIMat 1.1.06 [33], and Microsoft Excel for Microsoft 365 Version 2410 Build 16.0.18129.20158.  
This paper presents and discusses different charts within this science mapping, like the coupling maps chart, the co-occurrence networks chart, and the thematic evolution chart.