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Entrepreneurial education (EE) has grown rapidly and become important for how we prepare future generations for work. However, a less addressed piece of the puzzle is: who is the entrepreneurial educator? In this paper, our aim draws upon Palmer’s (1998) idea of wholeness, where we seek to connect the two problem spaces of the role diversity of entrepreneurial educators and how the educator is closely tied to the evolutionary development of the domain in constructing signature pedagogies that could cater for the issue of a low capitalization rate.

We problematize on a conceptual level the entry-level personas that have been addressed in prior literature by using the processes of transferability and evolutionary theory to tease out how variability among educators could create antecedents to form a signature pedagogy.

We recognize four archetypes: the experienced entrepreneur, the entrepreneurship scholar, the academic scholar and the pedagogical expert. Our problematization on the variance of the four archetypes provides a fertile ground to engage in addressing “the elephants in the room” in EE, the capitalization rate of graduates and the importance of developing a signature pedagogy, acknowledged in other domains, to enhance legitimacy.

The paper provides a nuanced outlook on who is teaching in the entrepreneurial classroom and problematizes from an evolutionary perspective the potential future paths to continue staying relevant for society as well as justifying our presence as a legit domain in academia.

In this chapter, we review the historical development of elite theory, and then we propose a way forward beyond it. Elite theory emerged as a critique of democracy in the late 19th century. Although it used historical materials illustratively, it tended to be ahistorical theoretically because its primary aim was to demonstrate the perdurance of elites even in conditions of mass suffrage. Lachmann was the first scholar to develop elite theory as a truly historical and explanatory framework by combining it with elements of Marxism. Even Lachmann's theory, however, remained inadequate because it did not rest on a fully articulated theory of power. In this introduction, we suggest a “relational power theory” as a remedy to this situation, and we use it to formulate a general heuristic for the study of elites, nonelites, and their interrelationships. To illustrate its utility, we show how it can illuminate the chapters in this volume (though they were not necessarily written for these purposes).

Artificial intelligence (AI) is poised to reshape mental health practices, policies and research in the coming decade. Simultaneously, mental health inequalities persist globally, imposing considerable costs on individuals, communities and economies. This study aims to investigate the impact of AI technologies on future citizenship for individuals with mental health challenges (MHCs).

This research used a community-based participatory approach, engaging peer researchers to explore the perspectives of adults with MHCs from a peer-led mental health organisation. This study evaluated potential threats and opportunities presented by AI technologies for future citizenship through a co-created film, depicting a news broadcast set in 2042. Data were gathered via semi-structured interviews and focus groups and were analysed using a reflexive thematic approach.

The analysis identified four key themes: Who holds the power? The divide, What it means to be human, and Having a voice. The findings indicate that adults with living experiences of MHCs are eager to influence the development of AI technologies that affect their lives. Participants emphasised the importance of activism and co-production while expressing concerns about further marginalisation.

This study provides new insights into the intersection of AI, technology and citizenship, highlighting the critical need for inclusive practices in technological advancement. By incorporating the perspectives of individuals with living experiences, this study advocates for participatory approaches in shaping AI technologies in mental health. This includes the co-creation of machine learning algorithms and fostering citizen engagement to ensure that advancements are equitable and empowering for people with MHCs.