The author examines the migration of Indiana University Libraries’ interlibrary loan platform, ILLiad, from a locally-hosted server to OCLC hosting through the perspective of a new department head inheriting this critical technology decision. He explores how staffing changes, lost institutional knowledge, recurring system instability, and limited technical capacity prompted a reassessment of long-standing local practices. The piece outlines research, consortium consultation, approval processes, implementation challenges, authentication and workflow issues, and post-migration tradeoffs. Ultimately, the author offers practical guidance for new leaders tasked with managing inherited systems, vendor relationships, imperfect information, and strategic change in complex academic library environments.
One of the generative artificial intelligence tools developed for use in libraries, including academic libraries, is the AI Primo Research Assistant. Of the 65 academic libraries in Poland, only 19 have access to software that supports this tool. In practice, only 9 libraries have implemented it (data from March 2025). For the purposes of this study, original research was conducted to assess the implementation status of the Primo Assistant in academic libraries in Poland. Two anonymous surveys were developed for this purpose and sent to libraries that had implemented the feature, as well as to those with the capability to run the Primo Assistant (i.e., the Primo VE Discovery admin role), in order to gather information on why they had chosen not to implement it. The analysis revealed several positive aspects, mainly a reduction in the workload of staff tasked with preparing publication lists on topics requested by library users. Some concerns were also raised by library employees, mainly regarding the reliability of the metadata provided and the accuracy of the recommended publications. The study also revealed a general lack of awareness and a need for further implementation. This paper presents the first scientific study focused on the implementation of the AI Primo Research Assistant in Polish academic libraries.
This study critically explores the transformative potential of human-computer interaction (HCI) in reimagining African public libraries as dynamic, user-centered, and culturally grounded spaces. Based on a literature review and comparative analysis of libraries across several African countries, the research investigates how HCI principles can enhance user engagement, usability, and inclusivity, particularly in multilingual, resource-constrained, and postcolonial contexts. The paper situates libraries as sociotechnical infrastructures that mediate between technology, local knowledge systems, and community needs, and argues for the importance of participatory and culturally responsive design approaches in library digitization efforts. The findings highlight significant gaps in current implementations of HCI within library services, including the lack of localized interfaces and limited user involvement in design processes. The study concludes by offering practical recommendations for integrating HCI into library development strategies and advocating for the co-creation of digital public spaces that reflect and empower Africa’s diverse knowledge ecologies. In doing so, the paper contributes to the growing discourse on decolonial approaches to technology and the future of public libraries in the digital age.
This study evaluates the performance of four generative AI models—ChatGPT, DeepSeek, Gemini, and Copilot—in generating descriptive metadata for bibliographic resources. Models were tested on a small, diverse set of resources using four prompt types: a basic prompt, a basic prompt with an example, a detailed prompt referencing Resource Description and Access (RDA) guidelines, and a detailed prompt with an example. Results show that both detailed RDA guidance and the inclusion of sample outputs improved metadata quality, particularly in formatting and field structure. While DeepSeek and ChatGPT showed better performance on the tasks, all models displayed limitations in parsing and following the prompts, using descriptive metadata fields, analyzing subject headings, and assigning URIs. These findings suggest that while generative AI holds potential to assist in metadata creation, its current capabilities fall short of meeting cataloging standards without human review.
Countless research questions arise when investigating connections between library resource discovery and student success. Existing literature explores best practices of database description language and style, the usability of database A-Z lists, and library resource jargon. Academic libraries continue to grapple with these challenges in resource discovery, even as online searching behavior evolves and new research tools emerge. A research team at the University of Arizona Libraries builds on the literature by examining these topics with a focus on the impact of a user's academic discipline, university affiliation (faculty, staff, or student), and research experience on their understanding of database terminology, resource content and applications, and A-Z list type filters. The authors conducted an environmental scan of library websites along with several usability tests to identify and reduce library and disciplinary jargon on theirA-Z list to make databases more understandable and approachable to all users. This article presents the results of these assessments as a case study for exploring external and internal factors that impact users' understanding and discovery of databases.
