Large Language Models and Public Libraries : Self-Assessed AI and LLM Competence Among Swedish Public Library Personnel

Sammanfattning

This thesis focuses on how Swedish public library personnel assess their competence in Artificial Intelligence (AI) and, in particular, Large Language Models (LLMs). Previous research highlights that libraries are increasingly expected to support AI literacy, promote digital inclusion, and guide citizens in navigating rapidly developing technologies. Yet, there is limited research on how public library personnel themselves assess their abilities and working conditions in this area. The study examines Swedish library personnel’s self-assessed AI and LLM competence, their ability to assist users with related queries, their perceived organisational conditions, and how these conditions may influence their self-assessed AI competence. The study applies a quantitative method in the form of an online survey distributed to Swedish public libraries. In total, 110 respondents participated. The analysis is based on Long and Magerko’s (2020) AI literacy framework, which was adapted into six AI literacy themes. The results show that respondents overall did not assess their AI literacy or ability to assist library users with related queries highly. Even though many of the respondents had used LLMs professionally, fewer felt confident explaining how the technology behind it works or discussing issues such as future societal implications. Access to workplace guidelines, training, tools and support was associated with higher confidence levels. Although, not everyone was aware of what support was available and guidelines varied between workplaces. The study concludes that public libraries may require clearer organisational support and further professional training to meet growing expectations regarding AI and LLM literacy and digital leadership.