Library professionals and library researchers use surveys for data collection from human subjects, involving a research methods skill set. However, creating quality surveys can be challenging, involving close attention to design and revision through validation processes. When survey design errors are not detected prior to data collection, the survey data is unreliable. Generative artificial intelligence tools could potentially assist library professionals and researchers with survey design error detection to enhance the quality of the data collected. This research tested the performance of five generative artificial intelligence tools in detecting two common survey question errors: double-barrelled and acronym-dependent questions. While the generative artificial intelligence tools were typically able to detect acronym-dependent questions, they underperformed in the detection of double-barrelled questions. Even the subsequent provision of explicit training on double-barrelled questions did not lead to full accuracy in detection across the generative artificial intelligence tools. Generative artificial intelligence tools cannot be relied on for this aspect of survey quality control at this stage of their evolution.

The work explores transformation of services of Ukrainian public libraries in response to the full-scale military invasion of Ukraine. It focuses on the role of libraries as vital community centers that offer shelter, humanitarian help, counseling and health services, and as entities fighting misinformation and documenting war crimes. The work reinforces librarianship's significance as a social service profession.

This study explores the opportunities and challenges of integrating robotics technologies into science-hub libraries in Zanzibar, focusing on three hubs located on Unguja Island. The study population included staff working in the hubs and stakeholders dealing with robotics technologies in the country. A case study research design with a qualitative research approach was employed to collect, analyze, and interpret the data. Qualitative data was gathered through focus group discussions to gauge the respondents’ perceptions and experiences regarding educational robotics technologies in the hub settings. The findings highlight promising opportunities, including government support through infrastructure investments; grants for science, technology, engineering, and mathematics education; a competence-based curriculum; and access to professional development such as online courses. However, key challenges persist, including limited robotics infrastructure, insufficient librarian training, and financial constraints. To address these issues, the study recommends securing long-term funding, advocating for the inclusion of robotics in Zanzibar's education curriculum, and introducing affordable mobile robotics kits.

This study examined the influence of demographic characteristics and technological skills on students’ perceived search self-efficacy and their actual search performance. A printed questionnaire was used to collect data on students’ demographic backgrounds and technological skills, search self-efficacy, and search performance on a set of task-based questions. Descriptive statistics were used to analyze the demographic data and technological skills, self-efficacy levels, and task performance scores, while inferential statistics were conducted to compare the self-efficacy and task performance scores across the groups. Additionally, Pearson's correlation was used to examine the relationships between various aspects of search self-efficacy and total task scores. The findings revealed that while students exhibited moderate search self-efficacy, they performed poorly on task-based questions. Significant differences in self-efficacy and task performance were observed based on demographic backgrounds and technological skill levels. Pearson's correlation identified weak but significant positive relationships between search self-efficacy and task scores, and strong positive correlations among the self-efficacy measures.

As digital transformation accelerates outsourcing adoption in university libraries, concerns emerge regarding core capability erosion in reading promotion services, yet systematic understanding of optimal insourcing–outsourcing boundary choices remains limited. This study employs crisp-set Qualitative Comparative Analysis to examine 2399 reading promotion activities from 19 Shanghai universities over the past 5 years, analyzing configurational pathways across four capability dimensions: core collections, distinctive services, data elements, and library spaces. Results reveal asymmetric advantages between service models: insourcing pathways achieve effectiveness through physical space–staff synergy (CO1–CO3, consistency = 0.897–1.000), emphasizing face-to-face interactions and professional relationships, while outsourcing pathways leverage technology–expertise coupling (SO1–SO3, consistency = 0.860–1.000), integrating virtual platforms with external knowledge resources. No single capability proves necessary for success, confirming configurational causality in reading promotion effectiveness. These findings demonstrate that sustainable competitive advantage emerges from capability-specific boundary delineation rather than wholesale outsourcing strategies, providing theoretical insights for capability-based outsourcing decisions and practical guidance for dynamic resource allocation in library digital transformation contexts.

The study synthesizes literature on opening the ‘black box’ of artificial intelligence (AI) for credible information retrieval systems (IRS), and the emerging roles for library and information science (LIS) professionals. A rapid review of literature was adopted for the study. Literature was retrieved from EBSCOHost and Proquest, complemented by Google Scholar, using three eligibility criteria of publication source, language and year of publication. The review found that the AI black box is opened by explainability, which enhances the transparency, fairness and credibility of the IRS. This reinforces users’ trust in the system based on their understanding of its internal workings. Thus, black box models adversely affect the deployment of IRS, demanding explainable AI in IRS. The study proposed emerging roles for LIS professionals (including advocacy for transparent models in IRS, algorithm education, facilitating open science and data curating), in opening the AI black box for credible IRS.

This article offers a critical and integrative review of how artificial intelligence (AI) is being incorporated into academic library systems, particularly in the context of scientific research production. Based on 29 studies, the review explores ethical practices, institutional boundaries, and epistemological challenges surrounding AI adoption. Findings reveal that AI is reshaping scholarly workflows, such as metadata creation, information retrieval, and literature review, while also introducing unresolved ethical concerns, including data privacy, algorithmic bias, academic integrity, and diminished human agency. The study identifies a misalignment between the rapid pace of AI implementation and the capacity of academic institutions to regulate its use responsibly. Librarians are situated at the intersection of innovation and ethical mediation, often without formal training or institutional support. The review concludes that AI should not be viewed merely as a functional tool but as a socio-technical agent requiring ethical governance, critical AI literacy, and structural accountability across academic ecosystems.

This study aimed to identify the prospects of green librarianship in academic settings. It also intended to reveal best practices for the adoption of green libraries. A systematic literature review methodology was incorporated to address the study's objectives. Twenty-two studies (peer-reviewed research articles and conference papers) published in 11 reputable digital databases were selected to conduct the investigation. The study revealed five critical prospects for green librarianship within academia: advocacy, innovation, institutional devotion, sociocultural empowerment, and transformative education. Initiatives like institutional planning, resource acquisition, community development, infrastructure and environmental education were found to be most useful in adopting green libraries. The study contributes to theory and practice by formulating specific managerial, methodological, and social implications.

Research on organizational communication in libraries has not been extensively conducted. This study aims to evaluate the current state of research on this subject. A systematic literature review method was utilized. The inclusion criteria focused on studies of organizational communication in libraries and research papers written in English. Searches were conducted in Scopus and ScienceDirect on 14 July 2025. To assess the risk of bias in the included studies, a quality assessment was employed. The analysis involved a thorough reading of the articles, followed by the creation of categories based on their content. Guided by the PRISMA framework, 27 articles published between 1995 and 2025 were analyzed. This review identifies two main categories, i.e. general studies and focused studies examining specific aspects of organizational communication. Most studies employed quantitative methods with descriptive statistics, while qualitative and mixed methods remain underutilized. It proposes practical and policy-level recommendations.

There has been a rapid development of information and communications technologies and, with the advent of the Internet of Things (IoT), artificial intelligence, and machine learning, massive disruption has been observed in various sectors, including education and libraries. These new technologies are interconnected and have revolutionized how electronic devices interact, exchange data, enable seamless automation, and provide exceptional user experiences by learning through user data. There is a huge market for IoT technologies, which has never been seen before with artificial intelligence integration. Despite this potential, the adoption of IoT in academic libraries faces several challenges, including high initial investment costs, a lack of standardized protocols, and security and privacy concerns. This study investigates the extent of adoption of IoT technologies and IoT-based services, and the challenges associated with their implementation and integration in university libraries across the United Arab Emirates. The study uses a descriptive quantitative research design with an online survey covering 66 university libraries. The findings reveal that there is a presence of all IoT technologies in university libraries across the United Arab Emirates, with radio-frequency identification and user-centric IoT applications such as self-checkout systems and mobile applications for various library services being widely used. On the otherhand, advanced technologies like artificial-intelligenceequipped IoT applications which includes augmented reality, virtual reality, drones, robots, and energy management systems are underutilized and are in a nascent stage. The key challenges identified include financial constraints, cybersecurity risks, organizational barriers, and a shortage of skilled personnel. The study provides various recommendations for policymakers and library administrators, including the need for a clear IoT adoption framework and policies, phased implementation, robust security measures, and staff training. By addressing these challenges, university libraries in the United Arab Emirates and other libraries in the Gulf Cooperation Council region and beyond can enhance their operational efficiency, users’ experiences, and sustainability with the adoption of IoT technologies.