This paper aims to examine the perceptions and experiences of AI in the European youth work sector. The purpose is to establish a baseline for what the current understanding of AI might be and what future youth workers and youth practitioners’ centered support is needed to ensure that any AI policymaking, education, and training actions are based on knowledge and relevant to the sector. This is to be achieved through the application of (1) desk literature review; (2) youth workers and youth work professionals centered data collection (e.g., interviews, focus groups, survey); (3) followed by an application of inductive reasoning and Constructivist Grounded Theory (Charmaz, 2014). The following questions guided this research study:

• What is known about the presence and impact of AI in the European youth field (and non-formal education sector)?
• What are youth practitioners’ and youth workers’ perceptions of AI in their youth work practice?
• What can be learned about possible challenges, needs, and opportunities related to the use of AI in youth work?
• Moving forward, what support is needed to ensure that AI is implemented in line with youth practitioners’ and youth workers’ needs?

This study aims to take a closer look at the experiential and practical aspects of how AI is perceived and experienced in the youth field. In other words, it is not about what AI is technically, but about how youth workers interpret AI presence in their work and how/if its presence impacts the youth field (e.g., the quality of youth work). In line with the grounded theory approach, the emphasis here is on the study’s participants’ experiences and interactions (Idrees et al., 2011).

This paper begins by an overview of the methodology and the data collection methods used. The findings are then presented in three sections: (1) Youth workers perceptions of AI; (2) Perceived benefits of AI in youth work; (3) Perceived issues and concerns about AI in youth work. This is followed by a set of considerations for the youth sector and researchers. The final section provides examples of existing AI resources and projects that might be useful in the youth work context.

The paper offers some early insights into a topic and educational practice that has been largely under-researched — and continues to undergo a rapid transformation. Therefore, it is important to note that the findings of this study provide a glimpse into the multilayered debate about what AI is and how it is perceived in the youth sector. The analysis here is based on a small sample of participants and should therefore not be viewed as representative of the entire sector. Nonetheless, the input of those involved in this study have been important to starting this conversation and identifying some common AI related themes, hopes, and fears in the European youth sector. Despite its limitations, there is a hope that the findings will provide new knowledge and be useful for youth policymakers, researchers, and the youth workers themselves.