Parallel Session 10 – AI and Library Service Development II
Date: Thursday, 2 July 2026, from 09:00 to 10:30
Moderator: TBC
Location: R7
10.1) Navigating Change: Academic Libraries and AI
Presenter: Mauritza Jadefrid, Linnaeus University, Sweden
Since the release of ChatGPT in November 2022, discussions about Artificial Intelligence (AI) in academic libraries have expanded. Many academic libraries are undergoing transformation in everyday work practices, interacting with technology. At the same time as generative AI (GenAI) continues to develop, academic librarians are trying to figure out when and how GenAI is relevant for the library. Academic libraries are considered reliable and trustworthy, and implementing AI into the library comes with many questions. The gap between traditional library roles and modern knowledge environments creates tensions in keeping core values within the libraries and maintaining necessary innovation.
In my doctoral study, I explore how four Swedish academic libraries navigate change. The purpose of the thesis is to investigate how academic libraries engage in and contribute to shaping understandings of AI as part of their activities. I want to understand in what ways, if any, artificial intelligence is becoming something to be implemented in the activities of academic libraries. I also want to understand to what extent and in what ways artificial intelligence is being shaped and negotiated in academic libraries.
In this presentation, I will share the work in progress from my doctoral project. The study has a qualitative approach and is based on a sociomaterial perspective on technology in organisations. The starting point for the methodological study is conversations with participants about their practices in relation to AI, based on their roles and tasks at the library. Semi-structured interviews, follow-up conversations, and field notes from participants’ meetings and workshops constitute the methods used in the thesis.
I have four questions I want to discuss:
- How does AI manifest itself as a strategic issue in the context of academic libraries?
- How do library staff describe their role in relation to AI implementation, and what issues arise regarding ethical responsibility when AI is implemented in the information environment of academic libraries?
- How are issues of learning and competence expressed in relation to AI in academic libraries?
- How does AI take shape in library work processes and activities, and how is the technology reconfigured through staff and organisational interactions?
These four questions could be described by the concepts of strategy, activities, ethics, and competence. Strategy deals with issues of power, direction, values, and ethics. Activities encompass action, application, and the technology used. Ethics concern’s identity, ethical issues, and professional culture. Finally, competence covers issues related to change, knowledge, and reflection.
10.2) Remodeling Library Services through Artificial Intelligence
Presenter: Adeyemi Adewale Akinola, Mountain Top University, Nigeria
Artificial Intelligence (AI) is a field of computer science that focuses on automating tasks typically requiring human intelligence and interaction, and it has brought about a significant shift in information convergence and delivery in the digital age. Despite librarians’ efforts to introduce AI through library services, implementation and adaptability remain challenges.
The study examines how AI remodels library services. It outlines and applies four (4) main AI potentials of libraries as a model to establish the applicability of the study. The four (4) main AI potentials are (1) Improved User Experience, (2) improved services, (3) data analysis, and (4) AI Literacy (Training and Development). The study adopts a qualitative research design supported by literature synthesis and comparative evaluation of existing studies to explore the integration of Artificial Intelligence (AI) in remodeling library services. The study selected key studies on remodeling library services through AI from Africa, Asia, and European countries. Selected data primarily focused on academic journal articles, institutional case studies, policy documents, conference proceedings, and industry reports published between 2020 and 2025.
The study reveals that chatbots in academic libraries greatly improved service delivery by providing users with real-time assistance. It also indicates that AI technology can automate repetitive tasks, which include cataloguing and classification work and basic reference question response. The study demonstrates that academic libraries can achieve usage pattern analysis, resource distribution knowledge, and user interest understanding through big data analytics. Results also show that librarians can create AI literacy programs to address faculty concerns about generative AI.
In conclusion, artificial intelligence-based library service remodeling stands as a strategic transformation of library operations for the modern era beyond its role as a technological advancement. The implementation of this system requires libraries to prepare themselves through staff training, ethical planning, strategic plans, professional development, and institutional readiness. Digital literacy gaps, together with data privacy concerns, infrastructure issues, and policy restrictions, need to be resolved for sustainable and fair technology implementation.
10.3) Using LLMs to Design an Indicator for Clinical Relevance
Presenter: Silvia Fattori, Vrije Universiteit Amsterdam, The Netherlands
Working in a research intelligence team within our university library, we often support research departments in medicine to explore and interpret trends in their research output. Increasingly, these research departments want to have more insight into the relevance of their academic work for application to societal issues. In the field of medicine, clinical relevance is a key component of such societal impact. Our current measures of clinical relevance include authorships of and citations in clinical guidelines. These measures are valuable, but cannot convey important gradations of relevance, and only measure relevance ex post. In collaboration with researchers from the Cancer Center Amsterdam (CCA) and with a researcher experienced in computational modeling, we propose a new framework where we use the capabilities of modern LLMs to categorize academic publications into user-defined levels of clinical relevance and apply this to the output of CCA . Our approach can potentially be adapted to other research areas, providing a more nuanced measure of societal impact. This work could also contribute to the development of new research metrics and inform research policy decisions.
In the session, we will discuss the following points.
– How we conceptualized and validated the different levels of clinical relevance and how these levels were operationalized to work with the computational model. Our levels range from basic research using cell cultures to clinical work with human participants. These levels were validated by experts in cancer research.
– How we experimented with different LLMs and explored various prompt engineering techniques to improve the quality of the indicator, and how we evaluated the outcomes
– Preliminary results, limitations, ideas for future improvements, and other applications of the method in the context of academic libraries.