The importance of involved stakeholders and the resulting social networks in international VET cooperation is highlighted in a large number of empirical studies (see e.g., Gessler, 2019). While social network analysis has a broader range of empirical use cases in a wide variety of educational settings (see e.g., Hodge et al., 2020; Jan & Vlachopoulos, 2018), the state of research related to VET (see e.g., Ditchman et al., 2018) is found to a much lesser extent, as is the coverage of social capital within international VET research (see Gessler & Siemer, 2020; Siemer & Gessler, 2021). This paper focuses on the development of social capital in the course of network building and its sustainability in an international consortium using the case study of the funded Erasmus+ project AI Pioneers. The following research questions will be pursued in the context of the submission:

1)    Which actors play a central role at the beginning of network building in the field of vocational education and training?

2)    What is the intensity of the relationships over the course of the network formation?

This paper will draw on various theoretical approaches in network research to build the theoretical and conceptual framework of the study. As the funded project is an innovation project, the promoter model is used to apply the "content-related dimension of support" (Gessler & Siemer, 2020, p. 46) within the egocentric networks to be analysed with the roles of power promoter, expertise promoter, process promoter and relationship promoter (Witte, 1999). The basic idea of the promoter model is the assumption that promoters are able to overcome barriers in the innovation process due to suitable resources, which has a promising effect on the implementation of projects (e.g., Gessler, 2019; Witte, 1999). Furthermore, the differentiation between bridging and bonding social capital, and thus the intensity of relationships, is crucial (Patulny & Svendsen, 2007). The distinction between strong and weak ties can be traced back to Granovetter (1973). Following on from Granovetter (1973), Marsden and Campbell (1984) empirically addressed the question of which further indicators can be used to capture the strength of social relationships. The authors primarily identified indicators for the closeness of relationships and also refer to the frequency of contact as well as the duration described as the years of acquaintance (Marsden & Campbell, 1984). Following on from this, Gessler and Siemer (2020; see also Siemer & Gessler, 2021) introduce a specification in the form of a level model for measuring the closeness of social relationships which is applied in the course of the present work: 1) Pure exchange of information, 2) Mutual exchange but distanced, 3) Goal-oriented coordination, 4) Cooperation, and 5) Trust in each other. The different levels are characterised to varying degrees by the features of relevance, reciprocity, intentionality, interdependence and consistency, so thatthe intensity increases over the five levels and the attributes of the subordinate levels are to be assigned to the superordinate levels as well.

The focus of the funded Erasmus+ project AI Pioneers (funding period 2023-2025) promotes the use and teaching of Artificial Intelligence (AI) in adult and vocational education and training, with a total of 10 project partners from seven different EU countries involved in the AI Pioneers project network (Germany, Greece, Portugal, Italy, Spain, Cyprus, Estonia). The focus of the project, besides the development of policy recommendations, toolkits, implementation guidelines of AI use cases and guidelines for the ethical and trustworthy use of AI in education, is on the implementation and establishment of an international network of AI Pioneers so that educators, stakeholders, policy makers and education planners are addressed as reference points for the design and implementation of future education projects related to AI (see e.g., Attwell et al., 2023). As network formation is at the core of the AI Pioneers project, this article aims to contribute to recording the network structures and describe the social capital that has been created. For this purpose, we use the egocentric network analysis (Fuhse, 2018) and examine the development of the intensity of the established relationships over the duration of the project. The topic of defining the boundaries of networks is central to network research, although there is no clear consensus on how these are to be clearly defined and what meaning emerges from them (Häußling, 2009). Accordingly, we define the network to be analysed in this study along the thematic focus on the AI Pioneers project. Using egocentric network analysis, the individual project partners are asked about their relevant relationships in the project context, and thus focussing on a specific number of actors and relationships. We use a standardized guideline for data collection (Döring, 2023). In order to gather as much contextual knowledge as possible about the subsequently generated network maps, the standardized key questions are supplemented by further in-depth questions. According to Marsden and Campbell: "Egocentric network data describe the local social environments surrounding individual actors in a network – usually comprising one or more of each focal actor’s direct contacts (“alters”) and certain qualities of the dyadic relationships between that actor (“ego”) and the alters" (Marsden & Campbell, 2012, p. 18). The data is analyzed, evaluated and visualized using the VennMaker tool.

It can be expected that the project partners of the Erasmus+ project AI Pioneers and their relationships with relevant stakeholders, which have arisen in the project context and also relate to it in terms of content, will intensify over the course of the implementation and thus contribute to the sustainability of the network. Furthermore, it can be expected that the networks and thus the social capital of the project partners within the international consortium will differ significantly from one another, possibly due to the different partners and their relationships with project-relevant stakeholders as well as their experience in the implementation of international projects in the context of artificial intelligence in the educational field. Interesting results could also emerge with regard to the organisation of the role of the relationship promoter, as the size of the networks may depend strongly on the commitment of individuals and their networking skills in the context of vocational education and training. Beyond the research design presented here, future research with regard to the survey of overall networks as well as the associated quantitative key figures in terms of density and centrality would be particularly interesting to follow, as well as the perspective of long-term sustainability of the relationships established beyond the end of the project.

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