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Introduction
In retrospect, few could envision the complex situation higher education (HE) is at the start of 2026. Since 2022, Generative Artificial Intelligence has created a unique impact, not only of euphoria for AI-based tools promising a quick fix for research tasks, but also for problems with erroneous and biased results (Flenady & Sparrow, 2025).
In Norway, the landscape in HE is quite alike the rest of Europe; searching and testing for possible solutions, but still insecure in how to include AI literacy at all levels in organizations like universities. The situation is problematic since HE students in Norway have increased their use of AI in their studies from 37% to 72% in just two years (Kjelkenes et al., 2026), which will require a quick reaction from HE institutions. Another issue to be taken into consideration is the challenging ethical issues hidden inside Artificial Intelligence. For instance, which data is used to train AI or what are the resources needed, in the form of energy and water, during the entire life cycle of an AI service. In the middle of this tension, an insecurity has emerged in how human intelligence will respond to this almost autonomous entity (Guest et al., 2025).
In this context, AI literacy is fundamental for navigating our digital landscapes, but no public institutions seem to be equipped to train its users. Libraries therefore stand uniquely positioned to mediate this transition.
This abstract will present how a national project in Norway, aimed to build trust and competence in AI for libraries, addresses various aspects, including literacy, emotions and pedagogy, when developing courses with a train-the-trainer approach.
Public Libraries
The Norwegian government published a new strategy document/report stating the course for digitalization towards 2030 (Dig.Dir, 2024). By 2030, the entire public sector must use AI in their daily work, and the aim is to become “the world’s most digitalized country” (ibid). Hence, AI is viewed as a core tool for improving public services, strengthening resilience, and enabling a highly digitalized society. To reach the 2030 goals, digital competence and AI skills are essential. Citizens must be equipped to understand, evaluate, and meaningfully engage with AI systems that increasingly influence everyday life.
Historically, public libraries in Norway have played a central role in supporting citizens in technological progress. When the internet entered our everyday life, libraries became an essential learning arena – providing access and guidance. This may be seen as a direct parallel for today’s explosive AI use. If Norway is to reach the goals of AI usage, someone must lead in training citizens, and libraries once again have an opportunity to serve as forces for democratic equality.
The Role of Academic Libraries
Variation in how far library staff have come in adopting AI results in substantial differences in AI competence, both internally within libraries and across the library landscape. However, when it comes to AI, libraries have an important (societal) role in helping people access information and foster education and literacy. Focusing on university libraries more specifically, this also includes information literacy, publishing, and research data management (RDM). Libraries are increasingly taking a leading role in guiding and facilitating AI use in higher education (Ali & Richardson, 2025). On a societal level, knowledge-based AI usage is essential for upholding fundamental values such as freedom of expression and democracy. For all of the abovementioned tasks, AI has become relevant. Despite the abundance of accessible content on AI, there is a significant demand for developing practical AI skills within the library sector (Ali & Richardson, 2025).
AI and Emotions
As AI-teachers and advisors in Norwegian Academic libraries we have since 2022 met many AI users through holding AI workshops and advising students and researchers. In our experience, both users and librarians inherit and express a variety of emotions in relation to AI. During the first years of implementation, we have witnessed a tendency of evolvement. In the start, participants in our AI workshops often expressed anxiety and insecurity about using AI in research. Today's engagement and curiosity without as much anxiety and insecurity is predominant. AI-use is increasingly implemented, e.g. when it comes to language-editing within academic writing. Even as participants become more familiar with AI over time, they continue to express uncertainty across a wide range of AI related topics. Their levels of prior knowledge vary considerably, which means that feelings of insecurity persist—though they gradually diminish as they gain experience. This variation underscores the need for sustained professional development within libraries. As librarians, we will continue to support students, researchers, and the broader public, and doing so requires maintaining a psychologically safe learning environment where diverse skill levels, questions, and uncertainties can be addressed openly.
TAIKun
To bridge this gap in competence, we present the TAIKun development project (Tilrettelegging for kompetanseheving og AI-kunnskap; Facilitating Competence Development and AI Knowledge). TAIKun is funded by The Norwegian National Library, being and a collaboration project with five participants being university libraries from across Norway. This collaborative approach ensures that knowledge is not only disseminated but also contextualized to meet the specific needs of Norwegian libraries.
The goal of this project is to enhance the competence and confidence of library staff in AI and contribute to building trust in the face of new demands and expectations.
A central component of TAIKun is its practical train-the-trainer approach. Prior to the training sessions, participants gain access to a curated list of relevant resources on AI, regulatory frameworks, and a pedagogical toolkit. With this as a shared starting point, the participants then meet in person to discuss prepared cases and exchange experiences and learn from one another. These cases are divided into two main categories: those primarily relevant for librarians working closely with researchers, and those tailored to librarians who mainly support students. The latter group also includes public library staff, as many students rely on their local public library as their nearest point of librarian support. The pedagogical toolkit includes strategies for effective teaching, facilitation techniques, and methods for adapting content to different audiences. This approach ensures that trainers are not only knowledgeable about AI but also skilled in delivering that knowledge in an engaging and accessible way. The fact that the program is not focused on the specifics of AI tools increases its longevity.
Although the project is Norwegian, the train-the-trainer program that will be the project output?come out of the project iswill be applicable in other countries as well and will, hopefully, contribute to the demand for developing practical AI skills within the library sector (Ali & Richardson, 2025).
Conclusion and some takeaways so far
As TAIKun progresses, several key insights have emerged that highlight both the opportunities and challenges of implementing AI training in libraries. One of the most valuable aspects of the project is the use of case-based learning. By working through real-world scenarios tailored to the needs of different librarian groups, those supporting researchers and those supporting students, participants will be able to engage with practical, relatable challenges and training that is directly applicable to their daily work.
However, the project has also revealed issues with the pedagogy for AI training. Teaching AI concepts requires a careful balance between technical depth and accessibility. Participants arrive with varying levels of prior knowledge, which makes it challenging to design a one-size-fits-all approach. Additionally, the fast developments in AI make it difficult to create teaching materials that are relevant in the future. These challenges underscore the importance of including a pedagogical toolkit.
During the presentation we will not only expand more on the abovementioned issues, but we will also share the findings from the pilot testing of the train-the-trainer program.
References
Ali, M. Y., & Richardson, J. (2025). AI literacy guidelines and policies for academic libraries: A scoping review. IFLA Journal, 51(3), 588–599. https://doi.org/10.1177/03400352251321192
Dig-Department. (2024, September 26). Utnytte mulighetene i kunstig intelligens. Regjeringen.no. https://www.regjeringen.no/no/tema/statlig-forvaltning/it-politikk/ny-nasjonal-digitaliseringsstrategi/utnytte-mulighetene-i-kunstig-intelligens/id3054706/
Flenady, G., & Sparrow, R. (2025). Cut the bullshit: Why GenAI systems are neither collaborators nor tutors. Teaching in Higher Education, 31(1). https://doi.org/10.1080/13562517.2025.2497263
Guest, O., M. Suarez, B. Müller, E. van Meerkerk, A. Oude Groote Beverborg, R. de Haan, A. Reyes Elizondo, M. Blokpoel, N. Scharfenberg, A. Kleinherenbrink, I. Camerino, M. Woensdregt, D. Monett, J. Brown, L. Avraamidou, J. Alenda-Demoutiez, F. Hermans, and I. van Rooij (2025). Against the un-critical adoption of ‘AI’ technologies in academia.
Kjelkenes, R., Helle, M., Holme, T., Nielsen Hole, T., Ianke, P., Mehammer Krog, M., & Størset, H. (2026). Studiebarometer 2025 (Nos. 01–2026). Norwegian Directorate for Higher Education and Skills.